DataM and TFIDF.ipynb

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    "colab": {
      "name": "DataM_StudentDataset.ipynb",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# IMPORTS AND VISUALIZATION"
      ],
      "metadata": {
        "id": "NG7wPmJFc0R-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-y2znmU2aqwJ",
        "outputId": "ea94dbed-3585-473b-fa39-84f58ac327f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.models import Model\n",
        "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
        "#from keras.optimizers import RMSprop\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing import sequence\n",
        "#from keras.utils import to_categorical\n",
        "from keras.callbacks import EarlyStopping\n",
        "%matplotlib inline\n",
        "from nltk.tokenize import word_tokenize\n",
        "from __future__ import unicode_literals\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "import nltk\n",
        "nltk.download(\"wordnet\")\n",
        "!pip install hazm\n",
        "from hazm import *\n",
        "\n",
        "\n",
        "\n",
        "# Keras\n",
        "from keras import optimizers\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Dense, Input, Embedding, Dropout\n",
        "from keras.layers import GlobalMaxPool1D, MaxPooling1D, GlobalMaxPooling1D\n",
        "from keras.layers import CuDNNLSTM, LSTM, Bidirectional\n",
        "from keras.layers.convolutional import Conv1D\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
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        "id": "5Zkj5JYXbM9f",
        "outputId": "bc430de8-7cba-4604-ea24-904cc8bdc6cf"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting hazm\n",
            "  Downloading hazm-0.7.0-py3-none-any.whl (316 kB)\n",
            "\u001b[K     |████████████████████████████████| 316 kB 8.5 MB/s \n",
            "\u001b[?25hCollecting nltk==3.3\n",
            "  Downloading nltk-3.3.0.zip (1.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4 MB 43.3 MB/s \n",
            "\u001b[?25hCollecting libwapiti>=0.2.1\n",
            "  Downloading libwapiti-0.2.1.tar.gz (233 kB)\n",
            "\u001b[K     |████████████████████████████████| 233 kB 43.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk==3.3->hazm) (1.15.0)\n",
            "Building wheels for collected packages: nltk, libwapiti\n",
            "  Building wheel for nltk (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nltk: filename=nltk-3.3-py3-none-any.whl size=1394484 sha256=a1a1bda98d525b99da5641c3cb36c0e65b58ba9d91d78ff605c3cd8daa15a8b4\n",
            "  Stored in directory: /root/.cache/pip/wheels/9b/fd/0c/d92302c876e5de87ebd7fc0979d82edb93e2d8d768bf71fac4\n",
            "  Building wheel for libwapiti (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for libwapiti: filename=libwapiti-0.2.1-cp37-cp37m-linux_x86_64.whl size=154854 sha256=4430fcfc6a6afb2750af1e9c90002986ffc7855a1bd7c92d5f2d9891b7828ece\n",
            "  Stored in directory: /root/.cache/pip/wheels/ab/b2/5b/0fe4b8f5c0e65341e8ea7bb3f4a6ebabfe8b1ac31322392dbf\n",
            "Successfully built nltk libwapiti\n",
            "Installing collected packages: nltk, libwapiti, hazm\n",
            "  Attempting uninstall: nltk\n",
            "    Found existing installation: nltk 3.7\n",
            "    Uninstalling nltk-3.7:\n",
            "      Successfully uninstalled nltk-3.7\n",
            "Successfully installed hazm-0.7.0 libwapiti-0.2.1 nltk-3.3\n"
          ]
        },
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    {
      "cell_type": "markdown",
      "source": [
        "**Read data**"
      ],
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        "id": "oLKWM60tbCK9"
      }
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_excel(\"/content/sample_data/dataset for clustering.xlsx\")\n",
        "df"
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              "                                               Comment  Label\n",
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              "4                        من از نظم خوابگاه راضي نيستم.    1.0\n",
              "..                                                 ...    ...\n",
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        "# df = df.dropna(subset = [\"Comment\"], inplace=True)\n",
        "sns.countplot(df.Label)\n",
        "plt.xlabel('Label')\n",
        "plt.title('Number of label ')\n",
        "\n",
        "df.head()"
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            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
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              "                                             Comment  Label\n",
              "0      شهید بهشتی رو انتخاب کردم چون از هر نظر عالیه    3.0\n",
              "1  بعد از تحقیق بسیار، دقیقا بر اساس اساتید بهشتی...    3.0\n",
              "2            متاسفانه خیییلی زیاده بی نظمی و اين بده    1.0\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0bd4d717-3768-40c6-9cdc-2e2a5b8dd4f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0bd4d717-3768-40c6-9cdc-2e2a5b8dd4f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NORMALIZATION"
      ],
      "metadata": {
        "id": "GwwGdpZ7culm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('omw-1.4')\n",
        "import re\n",
        "from nltk.corpus import wordnet\n",
        "#حذف کاراکتر های تکراری\n",
        "class RepeatReplacer(object):\n",
        "    def __init__(self):\n",
        "        self.repeat_regexp=re.compile(r'(\\w*)(\\w)\\2(\\w*)')\n",
        "        self.repl=r'\\1\\2\\3'\n",
        "    def replace(self,word):\n",
        "        if(wordnet.synsets(word)):\n",
        "            return word\n",
        "        repl_word=self.repeat_regexp.sub(self.repl,word)\n",
        "        if(repl_word!=word):\n",
        "            return self.replace(repl_word)\n",
        "        else:\n",
        "            return repl_word\n",
        "\n",
        "\n",
        "replacer=RepeatReplacer()\n",
        "\n",
        "\n",
        "\n",
        "def clean(text):\n",
        "  #حذف الفبای انگلیسی .....حذف ارقام.......حذف کاراکتر های بی استفاده\n",
        "  unwanted_digit=['0','1','2','3','4','5','6','7','8','9','۰',\n",
        "                  '۱','۲','٣','۴','۵','۶','۷','۸','۹']#۰۱۲۳۴۵۶۷۸۹\n",
        "  for digit in unwanted_digit:\n",
        "    text=text.replace(digit,\"\")\n",
        "\n",
        "  unwanted_alpha=['a','b','c','d','e','f','g','h','i','j',\n",
        "                  'k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n",
        "  for alpha in unwanted_alpha:\n",
        "    text=text.replace(alpha,\"\")\n",
        "  \n",
        "  unwanted_punc=[\"؟\",'،','\"',\"'\",'=','@','&','%','.',',',':','\\\\','$','^',\n",
        "                 \n",
        "                 '<','>','!','?','{','}',';','\\n','\\t',\n",
        "\n",
        "                 '(',')','[',']','/','*','+','#','\\u200c',\n",
        "\n",
        "                 '\\ufeff','-','_','|',\"u200c\",\"\\u200c\"]\n",
        "  for punc in unwanted_punc:\n",
        "    text=text.replace(punc,\"\")\n",
        "  return text\n",
        "\n",
        "\n",
        "#نرمال سازی جملات با استفاده از هضم اصلاح نیم فاصله ها و تبدیل کلمات جمع به حالت مفرد\n",
        "def normalizeWhazm(text):\n",
        "  normalizer = Normalizer()\n",
        "  stemmer = Stemmer()\n",
        "  lema=Lemmatizer()\n",
        "\n",
        "  text=normalizer.normalize(text)\n",
        "  text=lema.lemmatize(text)\n",
        "  text=stemmer.stem(text)\n",
        "  return text\n",
        "\n",
        "\n",
        "\n",
        "def remove_emoji(text):\n",
        "    emoji_pattern = re.compile(\"[\"\n",
        "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "                           u\"\\U00002702-\\U000027B0\"\n",
        "                           u\"\\U000024C2-\\U0001F251\"\n",
        "                           \"]+\", flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', text)\n",
        "\n",
        "\n",
        "\n",
        "for x in df.index:\n",
        "    #print(x)\n",
        "    text = df.loc[x, \"Comment\"]\n",
        "    #print(text)\n",
        "    df.loc[x, \"Comment\"] = clean(text)\n",
        "    df.loc[x, \"Comment\"] = replacer.replace(df.loc[x, \"Comment\"])\n",
        "    df.loc[x, \"Comment\"] = normalizeWhazm(df.loc[x, \"Comment\"])\n",
        "    df.loc[x, \"Comment\"] =remove_emoji(df.loc[x, \"Comment\"])\n",
        "\n",
        "    print(df.loc[x, \"Comment\"])\n",
        "\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1RW5DK_cck4v",
        "outputId": "6f8ecda5-0f6d-42ef-99ad-f50c5207787c"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "شهید بهشتی رو انتخاب کردم چون از هر نظر عالیه\n",
            "بعد از تحقیق بسیار دقیقا بر اساس اساتید بهشتی رو اول انتخاب کردم چون اساتید بهتر دارد\n",
            "متاسفانه خیلی زیاده بی نظمی و این بده\n",
            "واقعا من یک نفر تو دانشگاه بهشتی رضایتی ندار\n",
            "من از نظم خوابگاه راضی نیس\n",
            "مسئولان سلب مسئولیت می‌کند و نارضایتی هس\n",
            "به حقوق دانشجو احترام نمی‌گذارند\n",
            "بهشتی فقط جاش خوبه بقیش بده\n",
            " من از نظر آموزشی و اساتیدی که داشتم به تمام دانشگاهای تهران ترجیحش مید\n",
            "آموزش برق ک خیلی با ادب و گل و عالی هستن\n",
            "از نظر اساتید حداقل تو رشته خودم جز دو سه نفرشون قاطع میتونم فقط بگم در جایگاه استادی نیستن و راضی نیستم از ان\n",
            "عرض کردم از لحاظ آموزشی استادام بهتر هستند نسبت به تمام دانشگاه‌ها تو رشته\n",
            "ایراد زیاد داره این دانشگاه\n",
            "حقیقتا منم خیلی از شرایطم راضی ا\n",
            "از اساتید رضایت دار\n",
            "امکانات دانشگاه خیلی ضعیف و بد اس\n",
            "مسیر راه رفتن در دانشگاه سخت است و امکاناتی وجود ندارد\n",
            "از وضعیت دسترسی و گرفتن کتاب در دانشگاه راضی نیس\n",
            "من ناراضی هستم از دانشگاه\n",
            "اینجا مزخرف‌ترین دانگاه هس\n",
            "همه چی رو به دم تحمیل می‌کند مثل موضوع پایان نامه و این خیلی مزخرفه\n",
            "دیگه این جا را انتخاب نمی‌کن\n",
            "من فعلا راضی هستم و خوبه\n",
            "امکانات دانشگاه خیلی ضعیف و بد \n",
            "در رابطه با زمان رفت و امد راضی هس\n",
            "امکان ورود در زمان‌های تعطیل خوبه\n",
            "دانشگاه در منطقه خوش اب و هوای قرار دارد\n",
            "مسیر رفت امد سخته و شیب دار\n",
            "طریقه اطلاع رسانی نمرات پیشرفته و به روز اس\n",
            "تعداد مناسب رستوران و بوفه دارد\n",
            "ارتباط مناسب دانشگاه با صنعت وجود دارد\n",
            "ازمایشگاه‌های خوبی داره\n",
            "اساتید با صنعت و شرکت‌ها ارتباط دارند که خیلی خوبه\n",
            "اخلاق و برخورد مناسب کارکن\n",
            "تنوع زیاد در رابطه با وجود گرایش‌ها و رشته‌های مختلف در دانشگاه وجود دارد\n",
            "عدم وجود دچرخه در فضای دانشگاه\n",
            "کم تنوعی غذای سلف\n",
            "از سلف ناراضی ا\n",
            "سلف در تابستان بسته اس\n",
            "شیب زیاد دانشگاه و عدم وجود تسهیلات مناسب در این رابطه\n",
            "نحوه برگزاری کلاس سمینار نامناسب اس\n",
            "تعداد واحد‌های که باید تا قبل ترم چهار بگذرانیم به درستی انتخاب نشده اس\n",
            "انتخاب استادان حل تمرین نامناسب اس\n",
            "عملکرد استاد حل تمرین ما اصلا خوب نیس\n",
            "تنوع استادان کم اس\n",
            "تنوع درس‌ها کم اس\n",
            "سوالات بعضی امتحان‌ها طوری است ک مطالب اصلی رها میشود و بر روی مطالب جزی و حفظی تمرکز میشود\n",
            "نحوه ارزیابی برخی از اساتید کاملا غلط اس\n",
            "وجود فضای مناسب برا ی برقراری ارتباط دانشجوها باه\n",
            "عدم درک اساتید از شرایط دانشجو وجود دارد\n",
            "مخالفت بی اندازه اساتید با کارکردن دانشجویان که خیلی بی منطقی اس\n",
            "عدم آمادگی و بیتوجهی بعضی از اساتید به تدریس\n",
            "کیفیت پاین کلاسهای آموزش\n",
            "تنوع کم درس‌ها برخلاف بقیه دانشگاه\n",
            "عدم وجود محیط مناسب و لابی برای نشستن و کار کردن در دانشکده کامپیوتر \n",
            "استاد خوب و بد همه جا هست و بیشتر مواقع تعریف خوب و بد بیشتر به خود دانشجو و تلاش بستگی داره\n",
            " امکانات ازمایشگاهیش که عالیه \n",
            "اینجا فوق العادست \n",
            "میخوای از یه جا جای دیگه بری سه ساعت باید راه بری کل وقتبره و اصلا راضی کنده نیس\n",
            "یه سرویس داخل دانشگاه نداره\n",
            "از نظر بروکراسی اداری و رسیدگی به امور دانشجو واقعا خیلی کم لطفی میکن مسئولین دانشگاه\n",
            "از نظر امکانات رفاهی اصلا خوب نیس\n",
            "بعضی از استاد‌ها واقعاعملکرد بد و مسخره‌ای دارند\n",
            "عدم عدالت در نمره دادن بعضی اساتید به دانشجو‌ها وجود دارد\n",
            "نحوه ارزیابی بعضی از اساتید واقعا منطقی وعادلانه نیس\n",
            "برخی از اساتید بین دانشجو‌ها تبعیض میگذارند\n",
            "فضای سبز قشنگی دارد دانشگاه\n",
            "بعضی قوانین عجیب و بدی دارد\n",
            "بوفه غذای مناسبی ندارد\n",
            "متاسفانه ارتباط گرم و صمیمی بین دانشجو و اساتید وجود ندارد\n",
            "زمانبندی ارائه سمینار و پایان نامه‌ها غیر منطقی بود\n",
            "وجود اساتید با موضوعات کاری مختلف باعث می‌شود دانشجو راحت‌تر به موضوع تحقیق مورد علاقه خود بپردازد\n",
            "رفتار بعضی از اساتید دوستانه و از سر دلسوزی می‌باشد\n",
            "تعداد زیادی از دانشجوها رو با بی فکری آموزش محور میکن\n",
            "قوانین موجود برای اموزش محور کردن دانشجو‌ها اصلا عادلانه نیس\n",
            "وضعیت امکانات کلاس‌ها مثل پروژکتور‌ها خوب نیس\n",
            "استاد‌های راهنما دانشجو رو مجبور میکن بیان تو حوزه‌ی دلخواه خودشون کار کند\n",
            "به نظرات دانشجوها توجهی نمی‌شود\n",
            "به نظرسنجی‌های دانشجو‌ها اهمیتی نمی‌دهند\n",
            "اینترنت دانشگاه ضعیف اس\n",
            "در دوران کرونا ارتباط با اساتید و دسترسی به اساتید بسیار سخت بود \n",
            "بارم بندی غلط در اکثر دروس \n",
            "نبودن نقشهی مناسب از محل ساختمانها و دانشکد\n",
            "وجود امکانات ورزشی مناسب در دانشگاه\n",
            "وجود اساتید جوان و آشنا به تکنولوژیهای روز\n",
            "توجه فراوان دانشگاه به مسائل فرهنگی و ورزشی خوب اس\n",
            "حراست که برای دانشجویان احترام قایل اس\n",
            "رفتار کارکنان دوستانه اس\n",
            "نبود و کمبود وسایل نقلیه برای جابجای دانشجویان در داخل دانشگاه باعث ایجاد سختی در رفت امد دانشجویان می‌شود\n",
            "ارتیاط غیرموثر و غیر صمیمی بین دانشجویان و اساتید خوب نیس\n",
            "بعضی از قوانین اموزشی واقعا غیر منطقی و ناعادلانه اس\n",
            "عدم ایجاد بستر مناسب برای شنیدن نظرات و انتقادات دانشجویان \n",
            "دانشگاه بزرگی اس\n",
            "بعضی اساتید واقعا دلسوز و باسواد و منطقی هستند\n",
            "فضای مناسب برای ایجاد رابطه‌های دوستی دارد\n",
            "از لحاظ تنوع دانشجویان مناسب اس\n",
            "دانشجو‌های پر تلاش ودرسخوان دارد\n",
            "عدم اطلاع رسانی درست قوانین \n",
            "ارائه نامناسب درس‌های تر\n",
            "اندازه کلاس‌ها مناسب اس\n",
            "خدا لعنتشون کنه اینقدر اذیت میکن\n",
            "اموزش حضوری سخته مجازی بهتره\n",
            "خیلی مضخرفه\n",
            "خیلی اذیت می‌کند\n",
            "وضعیت خوابگاه خیلی بد اس\n",
            "خیلی بد بود هفته‌ای پنج روز کلاس حضوری با ساعت‌های پراکنده \n",
            "از آموزش مجازی در خانه خسته شده‌ام و افسردگی گرفتم \n",
            "در امتحانات حضوری امتحانا سخت بود \n",
            "افتضاح افتضاح\n",
            "آموزش حضوری عالی بود و یکی از بهترین تجربه‌های من بود\n",
            "امتحانات حضوری سخت بود پدرمون درامده سر امتح\n",
            "آموزش حضوری خیلی خوب بود\n",
            "غذای بد به دانشجو‌ها میدن فقط دنبال سختگیری بی جا هستند\n",
            "تبعیض بین دانشجوها اس\n",
            " آدم تاسف میخوره که بعضیا استادا این ملکت اینطورین\n",
            "بله اساتید بین دانشجوها تبعیض قاعل میشوند و این مسعله به ضر ماست و منصفانه نیس\n",
            "غذای دانشگاه آشغاله \n",
            "مدرک الکی بدردنخور چون اخرش همه بیکار می‌ش\n",
            "سیستم اموزشی غلط و بی اساس دار\n",
            "وضعیت خابگاه‌ها بد است \n",
            "وضعیت کیفیت غذا بده\n",
            " آموزش بد و نامناسب اس\n",
            "این چه وضعیه غذای بسته بندی حذف شده \n",
            "مسئول غذا ماسک هم نداره\n",
            "از حق نگذریم که یک دانشگاه لوکس و بزرگه\n",
            "دانشگاه شهید بهشتی فقط فضای طبیعت و آب و هواش خوبه \n",
            "مدیریت خیلی خوبی دارد\n",
            "خیلی وضعیت افتضاحی هس\n",
            "چرا انقدر باید وقت دانشجو‌ها در صف غذا تلف شود\n",
            "مسئولین دانشگاه به حرف دانشجو‌ها گوش نمی‌دهند\n",
            "اخیرا غذا کمتر از تعداد داتشجو هاست و ماست یخ زده به بچه‌ها داده میشه\n",
            "تعداد سگ‌های خوابگاه خیلی زیاده و این موضوع اذیت کنده اس\n",
            "وقتی نظر دانشجو برای کسی مهم نیست چه فایده داره\n",
            "در آموزش حضوری مشکل دوری مسیر و صبح زود و بیدار شدن و دیربرگشتن به خونهاینا اذیت کنده بود\n",
            "یه ذره خجالت بکشن بعضی استادا شرم کنید خدا\n",
            "قوانین اموزش محور شدن خیلی مضخرفه\n",
            "شاهد افت کیفی عذاها در کنار کم حجم شدن آن هست\n",
            "افتضاح هیچ وقت روز اولی که اومدم خوابگاه بهشتی رو یادم نمیره تمام تصوراتم از از این دانشگاه خراب شد\n",
            "مسخره‌ترین روش توزیع غذارو بهشتی داره بعد از سال درس خوندن توی دانشگاه‌های مختلف تاحالا اینهمه بی نظمی و بی برنامگی و بی کیفیتی رو ندید\n",
            "فضای خیلی خوبی داره و محوطه دانشگاه خیلی زیبا هس\n",
            "سرعت اینتر نت دانشگاه خیلی کم است و این ازار دهنده اس\n",
            "رفت و آمد به دانشگاه خیلی سخته مترو از دانشگاه دوره\n",
            "در کل سطح علمی اساتید دانشگاه خوبه\n",
            "می‌شود برای انجام پروژه‌های صنعتی از اساتیدکمک گرفت و این خیلی خوبه\n",
            "حضور اساتید با فیلدها کاری متفاوت که کمک می‌کنه برای انتخاب موضوع دانشجو حق انتخاب داشته باشه\n",
            "محل تحصیل و اساتید دانشجویان پردیس و روزانه و شبانه یکی است و این تنوع خی خوبه\n",
            "در اعتراض به نمرات متاسفانه اساتید انعطاف پذیر نیستن\n",
            "به دلیل کرونا و مجازی بودن امتحان‌ها به شدت سخت بود و زمان امتحان هم خیلی کم بود \n",
            "فارغ التحصیل شدن در ارشد خیلی طولانی هس\n",
            "نظر دانشجو برای کسی مهم نیس\n",
            "فکری به حال اون غذاهای اشغال سلف‌ها نمیکند و واقعا افتضاحن\n",
            "فقط یاد گرفتن به پوش گیر بدن\n",
            "به فکر رفاه دانشجویان نیستند و اهمیت نمی‌دهند\n",
            "استادان بین دانشجو‌ها تبعیض قائل می‌شوند\n",
            "امکانات دانشگاه خوبه\n",
            "اساتیدبه دانشجو‌ها استرس وارد می‌کند\n",
            "وضعیت افتضاحه هم کیفیت غذا پاینه هم کم غذا میدن \n",
            "وضعیت خابگاه‌ها و غذا و آموزش بد و نامناسب است \n",
            "قیمت غذای سلف در تابستان خیلی زیاد و بی منطق اس\n",
            "دانشگاه بزرگی اس\n",
            "همایش‌های مفیدی در دانشگاه برگزار می‌شود\n",
            "اندازه کلاس‌ها مناسب اس\n",
            "بعضی از اساتید دلسوز و مهربان هستند\n",
            "قوانین بیخود و سختگیری‌های بی دلیل زیادی دارد\n",
            "نگهبانان دم در خوش برخورد هستند\n",
            "زیاد به پوش خاصی گیر نمیدهند و اذیت نمی‌کند\n",
            "رقابت سالم بین دانشجویان هس\n",
            "واقعا از مسئولین راضی نیس\n",
            "ازمایشگاه‌های خوبی و زیادی داره دانشگاه\n",
            "بعضی دروس ازمایشگاه ندارد\n",
            "دانشگاه در زمان مجازی خیلی کم امکانات دا\n",
            "بوفه زیاد داره و خیلی خوبه\n",
            "کیفیت اساتید دانشکده مدیریت بسیار بالاس\n",
            "بعضی اساتید دانشکده کامپیوتر ارزیابی منطقی ندارند\n",
            "دانشکده کامپیوتر قوانین سختگیرانه بی جا برای دانشجویان ارشد دارد\n",
            "خیلی بی فکر و بد دانشجویان ارشد را اموزش محور می‌کند\n",
            "کیفیت غذا راضی نیس\n",
            "قیمت غذا‌ها ی سلف مناسب اس\n",
            "در تابستان فقط یک وعده غذا می‌دهند و این بده\n",
            "ازمایشگاه‌ها امکانات خوبی دارند\n",
            "مسئول ازمایشگاه خوش برخورد اس\n",
            "از وجود راه‌های ارتباطی زیاد بین دانشجو و اساتید راضی هس\n",
            "بعضی اساتید امکانات کاراموزی دانشجو‌ها را بعد از فارغ التحصیلی فراهم می‌کند و به انشجو اهمیت می‌دهند\n",
            "بعضی اساتید واقعا به نظر دانشجویان اهمیت می‌دهند\n",
            "نظرسنجی‌ها درباره دانشگاه کم اس\n",
            "باید بیشتر به روح و روان دانشجو اهمیت دهند\n",
            "حجم استرس امتحانات و روش‌های ارزیابی اساتید زیاد و نامناسب اس\n",
            "بعضی اساتید واقعا روش‌های ارزیابی مناسبی دارند\n",
            "سرویس حمل نقل دانشگاه ضعیف اس\n",
            "به باغچه‌ها خوب می‌رسند\n",
            "اینکه پنجشنبه‌ها اجازه ورود به دانشگاه داریم خیلی خوبه\n",
            "رفت و امد به دانشگاه خیلی سخته\n",
            "مترو از دانشگاه دوره\n",
            "سطح اساتید دانشگاه خوبه\n",
            "میشه برای انجام پوژه‌های صنعتی از اساتید کمک گرفت \n",
            "اینکه محل تحصیل دانشجویان شبانه و روزانه و پردیس یک جا هست عالیه\n",
            "تفاوت بین دانشجویان پردیس و شبانه روزانه نیس\n",
            "وجود اساتیدی که تو حوزه خودشون دانش کافی و به روز دارند\n",
            "وجود وام‌های دانشجوی متنوع عالیه\n",
            "شیب دانشگاه خیلی زیاده\n",
            "مسیر دانشگاه تا خوابگاه شیب زیادی داره و پای ادم درد می‌گیره\n",
            "زمین دانشگاه همش شیب تند یا سراشیبی داره که اذیت کنده اس\n",
            "سختگیری زیاده \n",
            "اساتید که سخت گیرن هیچ قوانین دانشجو‌های ارشد هم خیلی سختگیرانه اس\n",
            "غذا سلف رو دوست ندارم همچنین قیمت غذا در تابستان زیاده\n",
            "از کیفیت غذا ی سلف راضی نیس\n",
            "برای مسیر‌های رفت امد داخل دانشگاه هیچ خدمات و سرویسی وجود ندارد\n",
            "مسیر دانشکده تا سلف خیلی زیاده و برگشتنش هم شیب زیادی داره و دوره\n",
            "از یه جای داخل دانشگاه بخوایم بریم یه جای دیگه مسیر‌ها دوره و شیب‌های دانشگاه زیاده \n",
            "مترو از دانشگاه دوره و امدن به دانشگاه سخته\n",
            "دوچرخه داخل دانشگاه نداره برای رفت و امد این مسیر‌های طولان\n",
            "روش‌های ارزیابی و نمره دهی بعضی اساتید منطقی نیس\n",
            "نمرات که اساتید به دانشجو‌ها میدهند راضی کنده نیس\n",
            "مسئولین بعضی از قسمت‌ها بسیار مسئولیت پذیر هستند\n",
            "من از مسئولین دانشکده خودم راضی هس\n",
            "تنوع غذای بوفه مرکزی خیلی خوبه\n",
            "پروژکتور‌های کلاس‌ها خیلی خوب عمل می‌کند\n",
            "سطح علمی تحقیقات و اساتید دانشگاه بالاس\n",
            "اینکه به دانشجو‌های ارشد و دکتری ازمایشگاه‌های تخصی اختصاص می‌دهند خیلی خوبه\n",
            "فضای دانشگاه خیلی سرسبزه\n",
            "فضای دانشگاه و بزرگ بودن و سرسبز بودنشو دوست دار\n",
            "فضای دانشگاه خوبه هواشم تمیزه\n",
            "تنوع غذا در بوفه زیاد و خوبه\n",
            "بعضی از غذا‌های بوفه مثل پیتزاش عالیه\n",
            "دسترسی به غذا تو دانشگاه هسونه چون تعداد بوفه‌ها زیاده کیفیتشون هم بدک نیس\n",
            "غذا‌های سلف کمیت و کیفیتشون مشکل داره\n",
            "کالا کیفیت غذای سلف اکثر دانشگاه‌ها راضی کنده نیس\n",
            "قوانین موجود در دانشکده کامپیوتر برای دانشجویات ارشد بی منطق و مسخره اس\n",
            "از غذا قوانین و رفتار بعضی مسئولین راضی نیس\n",
            "رفتار نامناسب از اساتید ندید\n",
            "قوانین ورود و خروج به دانشگاه از نظر زمانی خوبه\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Comment  Label\n",
              "0      شهید بهشتی رو انتخاب کردم چون از هر نظر عالیه    3.0\n",
              "1  بعد از تحقیق بسیار دقیقا بر اساس اساتید بهشتی ...    3.0\n",
              "2              متاسفانه خیلی زیاده بی نظمی و این بده    1.0\n",
              "3       واقعا من یک نفر تو دانشگاه بهشتی رضایتی ندار    1.0\n",
              "4                         من از نظم خوابگاه راضی نیس    1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd348818-5f7d-4b18-b9dd-8793a1cebd97\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Comment</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>شهید بهشتی رو انتخاب کردم چون از هر نظر عالیه</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بعد از تحقیق بسیار دقیقا بر اساس اساتید بهشتی ...</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>متاسفانه خیلی زیاده بی نظمی و این بده</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>واقعا من یک نفر تو دانشگاه بهشتی رضایتی ندار</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>من از نظم خوابگاه راضی نیس</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd348818-5f7d-4b18-b9dd-8793a1cebd97')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd348818-5f7d-4b18-b9dd-8793a1cebd97 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cd348818-5f7d-4b18-b9dd-8793a1cebd97');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DELETE STOP WORDS"
      ],
      "metadata": {
        "id": "Id2mpMSQdRW2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path2='/content/sample_data/my_stop_words (1).txt'\n",
        "data = pd.read_csv(path2, header = None)\n",
        "#در ابتدا هر سطر را توکنایز میکنیم بعد استاپ وورد ها را حذف میکنیم و درون دیتاست قرار میدهیم\n",
        "def TokDelStopW(text):\n",
        "  z=[]\n",
        "  text=word_tokenize(text)\n",
        "\n",
        "  for i in range(len(text)):\n",
        "    \n",
        "    for x in data.index:\n",
        "      \n",
        "      if data.loc[x,0] == text[i]:\n",
        "        z.append(i)\n",
        "        break\n",
        "        \n",
        "  temp=[]\n",
        "  for j in z:\n",
        "    temp.append(text[j])\n",
        "  text = [x for x in text if x not in temp]\n",
        "  return \" \".join(text)\n",
        "  \n",
        "for x in df.index: \n",
        "  df.loc[x, \"Comment\"] = TokDelStopW(df.loc[x, \"Comment\"])\n",
        "  print(df.loc[x, \"Comment\"])\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w1EXdlFpdPzO",
        "outputId": "6b076d2c-37b7-4d35-8990-6898de26251c"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "شهید انتخاب کردم عالیه\n",
            "بعد تحقیق بسیار دقیقا اساس اساتید اول انتخاب کردم اساتید\n",
            "نظمی بده\n",
            "یک نفر رضایتی ندار\n",
            "نظم خوابگاه راضی\n",
            "مسئولان سلب مسئولیت می‌کند نارضایتی\n",
            "حقوق دانشجو احترام نمی‌گذارند\n",
            "جاش بقیش بده\n",
            "آموزشی اساتیدی تمام دانشگاهای تهران ترجیحش مید\n",
            "آموزش برق ک ادب عالی هستن\n",
            "اساتید حداقل رشته جز سه نفرشون قاطع میتونم بگم جایگاه استادی نیستن راضی نیستم\n",
            "عرض کردم لحاظ آموزشی استادام نسبت تمام دانشگاه‌ها رشته\n",
            "ایراد\n",
            "حقیقتا منم شرایطم راضی\n",
            "اساتید رضایت\n",
            "امکانات ضعیف\n",
            "مسیر راه رفتن سخت امکاناتی\n",
            "دسترسی کتاب راضی\n",
            "ناراضی\n",
            "مزخرف‌ترین دانگاه\n",
            "دم تحمیل می‌کند موضوع پایان نامه مزخرفه\n",
            "دیگه جا انتخاب نمی‌کن\n",
            "راضی\n",
            "امکانات ضعیف\n",
            "رابطه زمان راضی\n",
            "امکان ورود زمان‌های تعطیل\n",
            "منطقه خوش اب هوای قرار\n",
            "مسیر سخته شیب\n",
            "طریقه اطلاع رسانی نمرات پیشرفته روز\n",
            "تعداد مناسب رستوران بوفه\n",
            "ارتباط مناسب صنعت\n",
            "ازمایشگاه‌های خوبی\n",
            "اساتید صنعت شرکت‌ها ارتباط\n",
            "اخلاق برخورد مناسب کارکن\n",
            "تنوع رابطه گرایش‌ها رشته‌های مختلف\n",
            "دچرخه فضای\n",
            "تنوعی غذای سلف\n",
            "سلف ناراضی\n",
            "سلف تابستان بسته\n",
            "شیب تسهیلات مناسب رابطه\n",
            "نحوه برگزاری کلاس سمینار\n",
            "تعداد واحد‌های قبل ترم بگذرانیم درستی انتخاب\n",
            "انتخاب استادان حل تمرین\n",
            "عملکرد استاد حل تمرین\n",
            "تنوع استادان\n",
            "تنوع درس‌ها\n",
            "سوالات امتحان‌ها طوری ک مطالب اصلی رها میشود روی مطالب جزی حفظی تمرکز میشود\n",
            "نحوه ارزیابی برخی اساتید کاملا غلط\n",
            "فضای مناسب برا ی برقراری ارتباط باه\n",
            "درک اساتید شرایط دانشجو\n",
            "مخالفت اندازه اساتید کارکردن دانشجویان\n",
            "آمادگی بیتوجهی اساتید تدریس\n",
            "کیفیت پاین کلاسهای آموزش\n",
            "تنوع درس‌ها برخلاف بقیه\n",
            "محیط مناسب لابی نشستن کار کامپیوتر\n",
            "استاد جا بیشتر مواقع تعریف بیشتر دانشجو تلاش بستگی\n",
            "امکانات ازمایشگاهیش عالیه\n",
            "فوق العادست\n",
            "میخوای جا جای دیگه بری سه ساعت راه بری کل وقتبره راضی\n",
            "سرویس داخل نداره\n",
            "بروکراسی اداری رسیدگی امور دانشجو لطفی مسئولین\n",
            "امکانات رفاهی\n",
            "استاد‌ها واقعاعملکرد مسخره‌ای\n",
            "عدالت نمره دادن اساتید دانشجو‌ها\n",
            "نحوه ارزیابی اساتید وعادلانه\n",
            "برخی اساتید دانشجو‌ها تبعیض میگذارند\n",
            "فضای سبز قشنگی\n",
            "قوانین عجیب بدی\n",
            "بوفه غذای مناسبی\n",
            "ارتباط صمیمی دانشجو اساتید\n",
            "زمانبندی ارائه سمینار پایان نامه‌ها غیر\n",
            "اساتید موضوعات کاری مختلف باعث می‌شود دانشجو راحت‌تر موضوع تحقیق مورد علاقه بپردازد\n",
            "رفتار اساتید دوستانه سر دلسوزی می‌باشد\n",
            "تعداد زیادی فکری آموزش محور\n",
            "قوانین موجود اموزش محور دانشجو‌ها عادلانه\n",
            "امکانات کلاس‌ها پروژکتور‌ها\n",
            "استاد‌های راهنما دانشجو مجبور بیان حوزه‌ی دلخواه خودشون کار کند\n",
            "نظرات توجهی نمی‌شود\n",
            "نظرسنجی‌های دانشجو‌ها اهمیتی نمی‌دهند\n",
            "اینترنت ضعیف\n",
            "دوران کرونا ارتباط اساتید دسترسی اساتید بسیار سخت\n",
            "بارم بندی غلط اکثر دروس\n",
            "نبودن نقشهی مناسب محل ساختمانها دانشکد\n",
            "امکانات ورزشی مناسب\n",
            "اساتید جوان آشنا تکنولوژیهای روز\n",
            "توجه فراوان مسائل فرهنگی ورزشی\n",
            "حراست دانشجویان احترام قایل\n",
            "رفتار کارکنان دوستانه\n",
            "نبود کمبود وسایل نقلیه جابجای دانشجویان داخل باعث ایجاد سختی دانشجویان می‌شود\n",
            "ارتیاط غیرموثر غیر صمیمی دانشجویان اساتید\n",
            "قوانین اموزشی غیر ناعادلانه\n",
            "ایجاد بستر مناسب شنیدن نظرات انتقادات دانشجویان\n",
            "بزرگی\n",
            "اساتید دلسوز باسواد\n",
            "فضای مناسب ایجاد رابطه‌های دوستی\n",
            "لحاظ تنوع دانشجویان مناسب\n",
            "دانشجو‌های پر تلاش ودرسخوان\n",
            "اطلاع رسانی درست قوانین\n",
            "ارائه درس‌های\n",
            "اندازه کلاس‌ها مناسب\n",
            "خدا لعنتشون اینقدر اذیت\n",
            "اموزش سخته مجازی بهتره\n",
            "مضخرفه\n",
            "اذیت می‌کند\n",
            "خوابگاه\n",
            "هفته‌ای پنج روز کلاس ساعت‌های پراکنده\n",
            "آموزش مجازی خانه خسته شده‌ام افسردگی\n",
            "امتحانات امتحانا سخت\n",
            "افتضاح افتضاح\n",
            "آموزش عالی یکی بهترین تجربه‌های\n",
            "امتحانات سخت پدرمون درامده سر امتح\n",
            "آموزش\n",
            "غذای دانشجو‌ها دنبال سختگیری جا\n",
            "تبعیض\n",
            "آدم تاسف میخوره بعضیا استادا ملکت اینطورین\n",
            "بله اساتید تبعیض قاعل میشوند مسعله ضر منصفانه\n",
            "غذای آشغاله\n",
            "مدرک الکی بدردنخور اخرش بیکار می‌ش\n",
            "سیستم اموزشی غلط اساس\n",
            "خابگاه‌ها\n",
            "کیفیت غذا بده\n",
            "آموزش\n",
            "وضعیه غذای بسته بندی حذف\n",
            "مسئول غذا ماسک نداره\n",
            "حق نگذریم یک لوکس بزرگه\n",
            "شهید فضای طبیعت آب هواش\n",
            "مدیریت خوبی\n",
            "افتضاحی\n",
            "وقت دانشجو‌ها صف غذا تلف\n",
            "مسئولین حرف دانشجو‌ها گوش نمی‌دهند\n",
            "اخیرا غذا کمتر تعداد داتشجو هاست یخ زده بچه‌ها\n",
            "تعداد سگ‌های خوابگاه موضوع اذیت\n",
            "وقتی دانشجو کسی مهم نیست فایده\n",
            "آموزش مشکل دوری مسیر صبح بیدار دیربرگشتن خونهاینا اذیت\n",
            "ذره خجالت بکشن استادا شرم کنید خدا\n",
            "قوانین اموزش محور مضخرفه\n",
            "شاهد افت کیفی عذاها کنار حجم\n",
            "افتضاح هیچ وقت روز اولی اومدم خوابگاه نمیره تمام تصوراتم خراب\n",
            "مسخره‌ترین روش توزیع غذارو بعد سال درس خوندن توی دانشگاه‌های مختلف تاحالا اینهمه نظمی برنامگی کیفیتی ندید\n",
            "فضای خوبی محوطه زیبا\n",
            "سرعت اینتر نت ازار دهنده\n",
            "سخته مترو دوره\n",
            "کل سطح علمی اساتید\n",
            "می‌شود انجام پروژه‌های صنعتی اساتیدکمک\n",
            "حضور اساتید فیلدها کاری متفاوت کمک می‌کنه انتخاب موضوع دانشجو حق انتخاب باشه\n",
            "محل تحصیل اساتید دانشجویان پردیس شبانه یکی تنوع خی\n",
            "اعتراض نمرات اساتید انعطاف پذیر نیستن\n",
            "دلیل کرونا مجازی امتحان‌ها شدت سخت زمان امتحان\n",
            "فارغ التحصیل ارشد طولانی\n",
            "دانشجو کسی مهم\n",
            "فکری حال اون غذاهای اشغال سلف‌ها نمیکند افتضاحن\n",
            "یاد گیر بدن\n",
            "فکر رفاه دانشجویان نیستند اهمیت نمی‌دهند\n",
            "استادان دانشجو‌ها تبعیض قائل می‌شوند\n",
            "امکانات\n",
            "اساتیدبه دانشجو‌ها استرس وارد می‌کند\n",
            "افتضاحه کیفیت غذا پاینه غذا\n",
            "خابگاه‌ها غذا آموزش\n",
            "قیمت غذای سلف تابستان منطق\n",
            "بزرگی\n",
            "همایش‌های مفیدی برگزار می‌شود\n",
            "اندازه کلاس‌ها مناسب\n",
            "اساتید دلسوز مهربان\n",
            "قوانین بیخود سختگیری‌های دلیل زیادی\n",
            "نگهبانان دم خوش برخورد\n",
            "خاصی گیر نمیدهند اذیت نمی‌کند\n",
            "رقابت سالم دانشجویان\n",
            "مسئولین راضی\n",
            "ازمایشگاه‌های خوبی زیادی\n",
            "دروس ازمایشگاه\n",
            "زمان مجازی امکانات دا\n",
            "بوفه\n",
            "کیفیت اساتید مدیریت بسیار بالاس\n",
            "اساتید کامپیوتر ارزیابی\n",
            "کامپیوتر قوانین سختگیرانه جا دانشجویان ارشد\n",
            "فکر دانشجویان ارشد اموزش محور می‌کند\n",
            "کیفیت غذا راضی\n",
            "قیمت غذا‌ها ی سلف مناسب\n",
            "تابستان یک وعده غذا می‌دهند بده\n",
            "ازمایشگاه‌ها امکانات خوبی\n",
            "مسئول ازمایشگاه خوش برخورد\n",
            "راه‌های ارتباطی دانشجو اساتید راضی\n",
            "اساتید امکانات کاراموزی دانشجو‌ها بعد فارغ التحصیلی فراهم می‌کند انشجو اهمیت می‌دهند\n",
            "اساتید دانشجویان اهمیت می‌دهند\n",
            "نظرسنجی‌ها\n",
            "بیشتر روح روان دانشجو اهمیت\n",
            "حجم استرس امتحانات روش‌های ارزیابی اساتید\n",
            "اساتید روش‌های ارزیابی مناسبی\n",
            "سرویس حمل نقل ضعیف\n",
            "باغچه‌ها می‌رسند\n",
            "پنجشنبه‌ها اجازه ورود داریم\n",
            "سخته\n",
            "مترو دوره\n",
            "سطح اساتید\n",
            "انجام پوژه‌های صنعتی اساتید کمک\n",
            "محل تحصیل دانشجویان شبانه پردیس یک جا عالیه\n",
            "تفاوت دانشجویان پردیس شبانه\n",
            "اساتیدی حوزه خودشون دانش کافی روز\n",
            "وام‌های دانشجوی متنوع عالیه\n",
            "شیب\n",
            "مسیر خوابگاه شیب زیادی پای ادم درد می‌گیره\n",
            "زمین همش شیب تند یا سراشیبی اذیت\n",
            "سختگیری\n",
            "اساتید سخت گیرن هیچ قوانین دانشجو‌های ارشد سختگیرانه\n",
            "غذا سلف دوست همچنین قیمت غذا تابستان\n",
            "کیفیت غذا ی سلف راضی\n",
            "مسیر‌های داخل هیچ خدمات سرویسی\n",
            "مسیر سلف برگشتنش شیب زیادی دوره\n",
            "جای داخل بخوایم بریم جای دیگه مسیر‌ها دوره شیب‌های\n",
            "مترو دوره سخته\n",
            "دوچرخه داخل نداره مسیر‌های طولان\n",
            "روش‌های ارزیابی نمره دهی اساتید\n",
            "نمرات اساتید دانشجو‌ها میدهند راضی\n",
            "مسئولین قسمت‌ها بسیار مسئولیت پذیر\n",
            "مسئولین راضی\n",
            "تنوع غذای بوفه مرکزی\n",
            "پروژکتور‌های کلاس‌ها عمل می‌کند\n",
            "سطح علمی تحقیقات اساتید بالاس\n",
            "دانشجو‌های ارشد دکتری ازمایشگاه‌های تخصی اختصاص می‌دهند\n",
            "فضای سرسبزه\n",
            "فضای بزرگ سرسبز بودنشو دوست\n",
            "فضای هواشم تمیزه\n",
            "تنوع غذا بوفه\n",
            "غذا‌های بوفه پیتزاش عالیه\n",
            "دسترسی غذا هسونه تعداد بوفه‌ها کیفیتشون بدک\n",
            "غذا‌های سلف کمیت کیفیتشون مشکل\n",
            "کالا کیفیت غذای سلف اکثر دانشگاه‌ها راضی\n",
            "قوانین موجود کامپیوتر دانشجویات ارشد منطق مسخره\n",
            "غذا قوانین رفتار مسئولین راضی\n",
            "رفتار اساتید ندید\n",
            "قوانین ورود خروج زمانی\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('clean_data.csv')"
      ],
      "metadata": {
        "id": "msYq-X6IBetp"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BILD VOCABULARY"
      ],
      "metadata": {
        "id": "r4S-2rbqdj2N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "define vocabulary class"
      ],
      "metadata": {
        "id": "BrAtETsydqsk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#The first thing to do is to create values for our start of sentence,\n",
        "# end of sentence, and sentence padding special tokens\n",
        "\n",
        "class Vocabulary:\n",
        "    PAD_token = 0   # Used for padding short sentences\n",
        "    SOS_token = 1   # Start-of-sentence token\n",
        "    EOS_token = 2   # End-of-sentence token\n",
        "\n",
        "    def __init__(self, name):\n",
        "        self.name = name\n",
        "        self.word2index = {}  #a dictionary to hold word token to corresponding word index values\n",
        "        self.word2count = {}  #a dictionary to hold individual word counts (tokens, actually) in the corpus\n",
        "        self.index2word = { 0: \"PAD\", 1: \"SOS\", 2: \"EOS\"}  \n",
        "              #a dictionary holding the reverse of word2index (word index keys to word token values); special tokens added right away\n",
        "        self.num_words = 3             #this will be a count of the number of words (tokens, actually) in the corpus\n",
        "        self.num_sentences = 0         # this will be a count of the number of sentences \n",
        "                                       #(text chunks of any indiscriminate length, actually) in the corpus\n",
        "        self.longest_sentence = 0      # this will be the length of the longest corpus sentence by number of tokens\n",
        "\n",
        "    def add_word(self, word):\n",
        "        if word not in self.word2index:\n",
        "                                        # First entry of word into vocabulary\n",
        "            self.word2index[word] = self.num_words\n",
        "            self.word2count[word] = 1\n",
        "            self.index2word[self.num_words] = word\n",
        "            self.num_words += 1\n",
        "        else:\n",
        "                                          # Word exists; increase word count\n",
        "            self.word2count[word] += 1\n",
        "            \n",
        "    def add_sentence(self, sentence):\n",
        "        sentence_len = 0\n",
        "        for word in sentence.split(' '):\n",
        "            sentence_len += 1\n",
        "            self.add_word(word)\n",
        "        if sentence_len > self.longest_sentence:\n",
        "            # This is the longest sentence\n",
        "            self.longest_sentence = sentence_len\n",
        "        # Count the number of sentences\n",
        "        self.num_sentences += 1\n",
        "\n",
        "    def to_word(self, index):\n",
        "        return self.index2word[index]\n",
        "\n",
        "    def to_index(self, word):\n",
        "        return self.word2index[word]\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "BbpJ3-5rdnxe"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "instantiation a vocabulary class for whole dataset"
      ],
      "metadata": {
        "id": "vKG7BLxMdwb0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "voc_Doc = Vocabulary('doc')\n",
        "print(voc_Doc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjGdecy-euS0",
        "outputId": "6fce497a-44ca-4cab-e274-cfb02807647e"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<__main__.Vocabulary object at 0x7ff934fb7b90>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in df.index:\n",
        "    #print(x)\n",
        "    text = df.loc[x, \"Comment\"]\n",
        "    print(text)\n",
        "    voc_Doc.add_sentence(text)"
      ],
      "metadata": {
        "id": "Aic9t8FOe99D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46ad8314-e254-41d9-94c0-2c7c59c53100"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "شهید انتخاب کردم عالیه\n",
            "بعد تحقیق بسیار دقیقا اساس اساتید اول انتخاب کردم اساتید\n",
            "نظمی بده\n",
            "یک نفر رضایتی ندار\n",
            "نظم خوابگاه راضی\n",
            "مسئولان سلب مسئولیت می‌کند نارضایتی\n",
            "حقوق دانشجو احترام نمی‌گذارند\n",
            "جاش بقیش بده\n",
            "آموزشی اساتیدی تمام دانشگاهای تهران ترجیحش مید\n",
            "آموزش برق ک ادب عالی هستن\n",
            "اساتید حداقل رشته جز سه نفرشون قاطع میتونم بگم جایگاه استادی نیستن راضی نیستم\n",
            "عرض کردم لحاظ آموزشی استادام نسبت تمام دانشگاه‌ها رشته\n",
            "ایراد\n",
            "حقیقتا منم شرایطم راضی\n",
            "اساتید رضایت\n",
            "امکانات ضعیف\n",
            "مسیر راه رفتن سخت امکاناتی\n",
            "دسترسی کتاب راضی\n",
            "ناراضی\n",
            "مزخرف‌ترین دانگاه\n",
            "دم تحمیل می‌کند موضوع پایان نامه مزخرفه\n",
            "دیگه جا انتخاب نمی‌کن\n",
            "راضی\n",
            "امکانات ضعیف\n",
            "رابطه زمان راضی\n",
            "امکان ورود زمان‌های تعطیل\n",
            "منطقه خوش اب هوای قرار\n",
            "مسیر سخته شیب\n",
            "طریقه اطلاع رسانی نمرات پیشرفته روز\n",
            "تعداد مناسب رستوران بوفه\n",
            "ارتباط مناسب صنعت\n",
            "ازمایشگاه‌های خوبی\n",
            "اساتید صنعت شرکت‌ها ارتباط\n",
            "اخلاق برخورد مناسب کارکن\n",
            "تنوع رابطه گرایش‌ها رشته‌های مختلف\n",
            "دچرخه فضای\n",
            "تنوعی غذای سلف\n",
            "سلف ناراضی\n",
            "سلف تابستان بسته\n",
            "شیب تسهیلات مناسب رابطه\n",
            "نحوه برگزاری کلاس سمینار\n",
            "تعداد واحد‌های قبل ترم بگذرانیم درستی انتخاب\n",
            "انتخاب استادان حل تمرین\n",
            "عملکرد استاد حل تمرین\n",
            "تنوع استادان\n",
            "تنوع درس‌ها\n",
            "سوالات امتحان‌ها طوری ک مطالب اصلی رها میشود روی مطالب جزی حفظی تمرکز میشود\n",
            "نحوه ارزیابی برخی اساتید کاملا غلط\n",
            "فضای مناسب برا ی برقراری ارتباط باه\n",
            "درک اساتید شرایط دانشجو\n",
            "مخالفت اندازه اساتید کارکردن دانشجویان\n",
            "آمادگی بیتوجهی اساتید تدریس\n",
            "کیفیت پاین کلاسهای آموزش\n",
            "تنوع درس‌ها برخلاف بقیه\n",
            "محیط مناسب لابی نشستن کار کامپیوتر\n",
            "استاد جا بیشتر مواقع تعریف بیشتر دانشجو تلاش بستگی\n",
            "امکانات ازمایشگاهیش عالیه\n",
            "فوق العادست\n",
            "میخوای جا جای دیگه بری سه ساعت راه بری کل وقتبره راضی\n",
            "سرویس داخل نداره\n",
            "بروکراسی اداری رسیدگی امور دانشجو لطفی مسئولین\n",
            "امکانات رفاهی\n",
            "استاد‌ها واقعاعملکرد مسخره‌ای\n",
            "عدالت نمره دادن اساتید دانشجو‌ها\n",
            "نحوه ارزیابی اساتید وعادلانه\n",
            "برخی اساتید دانشجو‌ها تبعیض میگذارند\n",
            "فضای سبز قشنگی\n",
            "قوانین عجیب بدی\n",
            "بوفه غذای مناسبی\n",
            "ارتباط صمیمی دانشجو اساتید\n",
            "زمانبندی ارائه سمینار پایان نامه‌ها غیر\n",
            "اساتید موضوعات کاری مختلف باعث می‌شود دانشجو راحت‌تر موضوع تحقیق مورد علاقه بپردازد\n",
            "رفتار اساتید دوستانه سر دلسوزی می‌باشد\n",
            "تعداد زیادی فکری آموزش محور\n",
            "قوانین موجود اموزش محور دانشجو‌ها عادلانه\n",
            "امکانات کلاس‌ها پروژکتور‌ها\n",
            "استاد‌های راهنما دانشجو مجبور بیان حوزه‌ی دلخواه خودشون کار کند\n",
            "نظرات توجهی نمی‌شود\n",
            "نظرسنجی‌های دانشجو‌ها اهمیتی نمی‌دهند\n",
            "اینترنت ضعیف\n",
            "دوران کرونا ارتباط اساتید دسترسی اساتید بسیار سخت\n",
            "بارم بندی غلط اکثر دروس\n",
            "نبودن نقشهی مناسب محل ساختمانها دانشکد\n",
            "امکانات ورزشی مناسب\n",
            "اساتید جوان آشنا تکنولوژیهای روز\n",
            "توجه فراوان مسائل فرهنگی ورزشی\n",
            "حراست دانشجویان احترام قایل\n",
            "رفتار کارکنان دوستانه\n",
            "نبود کمبود وسایل نقلیه جابجای دانشجویان داخل باعث ایجاد سختی دانشجویان می‌شود\n",
            "ارتیاط غیرموثر غیر صمیمی دانشجویان اساتید\n",
            "قوانین اموزشی غیر ناعادلانه\n",
            "ایجاد بستر مناسب شنیدن نظرات انتقادات دانشجویان\n",
            "بزرگی\n",
            "اساتید دلسوز باسواد\n",
            "فضای مناسب ایجاد رابطه‌های دوستی\n",
            "لحاظ تنوع دانشجویان مناسب\n",
            "دانشجو‌های پر تلاش ودرسخوان\n",
            "اطلاع رسانی درست قوانین\n",
            "ارائه درس‌های\n",
            "اندازه کلاس‌ها مناسب\n",
            "خدا لعنتشون اینقدر اذیت\n",
            "اموزش سخته مجازی بهتره\n",
            "مضخرفه\n",
            "اذیت می‌کند\n",
            "خوابگاه\n",
            "هفته‌ای پنج روز کلاس ساعت‌های پراکنده\n",
            "آموزش مجازی خانه خسته شده‌ام افسردگی\n",
            "امتحانات امتحانا سخت\n",
            "افتضاح افتضاح\n",
            "آموزش عالی یکی بهترین تجربه‌های\n",
            "امتحانات سخت پدرمون درامده سر امتح\n",
            "آموزش\n",
            "غذای دانشجو‌ها دنبال سختگیری جا\n",
            "تبعیض\n",
            "آدم تاسف میخوره بعضیا استادا ملکت اینطورین\n",
            "بله اساتید تبعیض قاعل میشوند مسعله ضر منصفانه\n",
            "غذای آشغاله\n",
            "مدرک الکی بدردنخور اخرش بیکار می‌ش\n",
            "سیستم اموزشی غلط اساس\n",
            "خابگاه‌ها\n",
            "کیفیت غذا بده\n",
            "آموزش\n",
            "وضعیه غذای بسته بندی حذف\n",
            "مسئول غذا ماسک نداره\n",
            "حق نگذریم یک لوکس بزرگه\n",
            "شهید فضای طبیعت آب هواش\n",
            "مدیریت خوبی\n",
            "افتضاحی\n",
            "وقت دانشجو‌ها صف غذا تلف\n",
            "مسئولین حرف دانشجو‌ها گوش نمی‌دهند\n",
            "اخیرا غذا کمتر تعداد داتشجو هاست یخ زده بچه‌ها\n",
            "تعداد سگ‌های خوابگاه موضوع اذیت\n",
            "وقتی دانشجو کسی مهم نیست فایده\n",
            "آموزش مشکل دوری مسیر صبح بیدار دیربرگشتن خونهاینا اذیت\n",
            "ذره خجالت بکشن استادا شرم کنید خدا\n",
            "قوانین اموزش محور مضخرفه\n",
            "شاهد افت کیفی عذاها کنار حجم\n",
            "افتضاح هیچ وقت روز اولی اومدم خوابگاه نمیره تمام تصوراتم خراب\n",
            "مسخره‌ترین روش توزیع غذارو بعد سال درس خوندن توی دانشگاه‌های مختلف تاحالا اینهمه نظمی برنامگی کیفیتی ندید\n",
            "فضای خوبی محوطه زیبا\n",
            "سرعت اینتر نت ازار دهنده\n",
            "سخته مترو دوره\n",
            "کل سطح علمی اساتید\n",
            "می‌شود انجام پروژه‌های صنعتی اساتیدکمک\n",
            "حضور اساتید فیلدها کاری متفاوت کمک می‌کنه انتخاب موضوع دانشجو حق انتخاب باشه\n",
            "محل تحصیل اساتید دانشجویان پردیس شبانه یکی تنوع خی\n",
            "اعتراض نمرات اساتید انعطاف پذیر نیستن\n",
            "دلیل کرونا مجازی امتحان‌ها شدت سخت زمان امتحان\n",
            "فارغ التحصیل ارشد طولانی\n",
            "دانشجو کسی مهم\n",
            "فکری حال اون غذاهای اشغال سلف‌ها نمیکند افتضاحن\n",
            "یاد گیر بدن\n",
            "فکر رفاه دانشجویان نیستند اهمیت نمی‌دهند\n",
            "استادان دانشجو‌ها تبعیض قائل می‌شوند\n",
            "امکانات\n",
            "اساتیدبه دانشجو‌ها استرس وارد می‌کند\n",
            "افتضاحه کیفیت غذا پاینه غذا\n",
            "خابگاه‌ها غذا آموزش\n",
            "قیمت غذای سلف تابستان منطق\n",
            "بزرگی\n",
            "همایش‌های مفیدی برگزار می‌شود\n",
            "اندازه کلاس‌ها مناسب\n",
            "اساتید دلسوز مهربان\n",
            "قوانین بیخود سختگیری‌های دلیل زیادی\n",
            "نگهبانان دم خوش برخورد\n",
            "خاصی گیر نمیدهند اذیت نمی‌کند\n",
            "رقابت سالم دانشجویان\n",
            "مسئولین راضی\n",
            "ازمایشگاه‌های خوبی زیادی\n",
            "دروس ازمایشگاه\n",
            "زمان مجازی امکانات دا\n",
            "بوفه\n",
            "کیفیت اساتید مدیریت بسیار بالاس\n",
            "اساتید کامپیوتر ارزیابی\n",
            "کامپیوتر قوانین سختگیرانه جا دانشجویان ارشد\n",
            "فکر دانشجویان ارشد اموزش محور می‌کند\n",
            "کیفیت غذا راضی\n",
            "قیمت غذا‌ها ی سلف مناسب\n",
            "تابستان یک وعده غذا می‌دهند بده\n",
            "ازمایشگاه‌ها امکانات خوبی\n",
            "مسئول ازمایشگاه خوش برخورد\n",
            "راه‌های ارتباطی دانشجو اساتید راضی\n",
            "اساتید امکانات کاراموزی دانشجو‌ها بعد فارغ التحصیلی فراهم می‌کند انشجو اهمیت می‌دهند\n",
            "اساتید دانشجویان اهمیت می‌دهند\n",
            "نظرسنجی‌ها\n",
            "بیشتر روح روان دانشجو اهمیت\n",
            "حجم استرس امتحانات روش‌های ارزیابی اساتید\n",
            "اساتید روش‌های ارزیابی مناسبی\n",
            "سرویس حمل نقل ضعیف\n",
            "باغچه‌ها می‌رسند\n",
            "پنجشنبه‌ها اجازه ورود داریم\n",
            "سخته\n",
            "مترو دوره\n",
            "سطح اساتید\n",
            "انجام پوژه‌های صنعتی اساتید کمک\n",
            "محل تحصیل دانشجویان شبانه پردیس یک جا عالیه\n",
            "تفاوت دانشجویان پردیس شبانه\n",
            "اساتیدی حوزه خودشون دانش کافی روز\n",
            "وام‌های دانشجوی متنوع عالیه\n",
            "شیب\n",
            "مسیر خوابگاه شیب زیادی پای ادم درد می‌گیره\n",
            "زمین همش شیب تند یا سراشیبی اذیت\n",
            "سختگیری\n",
            "اساتید سخت گیرن هیچ قوانین دانشجو‌های ارشد سختگیرانه\n",
            "غذا سلف دوست همچنین قیمت غذا تابستان\n",
            "کیفیت غذا ی سلف راضی\n",
            "مسیر‌های داخل هیچ خدمات سرویسی\n",
            "مسیر سلف برگشتنش شیب زیادی دوره\n",
            "جای داخل بخوایم بریم جای دیگه مسیر‌ها دوره شیب‌های\n",
            "مترو دوره سخته\n",
            "دوچرخه داخل نداره مسیر‌های طولان\n",
            "روش‌های ارزیابی نمره دهی اساتید\n",
            "نمرات اساتید دانشجو‌ها میدهند راضی\n",
            "مسئولین قسمت‌ها بسیار مسئولیت پذیر\n",
            "مسئولین راضی\n",
            "تنوع غذای بوفه مرکزی\n",
            "پروژکتور‌های کلاس‌ها عمل می‌کند\n",
            "سطح علمی تحقیقات اساتید بالاس\n",
            "دانشجو‌های ارشد دکتری ازمایشگاه‌های تخصی اختصاص می‌دهند\n",
            "فضای سرسبزه\n",
            "فضای بزرگ سرسبز بودنشو دوست\n",
            "فضای هواشم تمیزه\n",
            "تنوع غذا بوفه\n",
            "غذا‌های بوفه پیتزاش عالیه\n",
            "دسترسی غذا هسونه تعداد بوفه‌ها کیفیتشون بدک\n",
            "غذا‌های سلف کمیت کیفیتشون مشکل\n",
            "کالا کیفیت غذای سلف اکثر دانشگاه‌ها راضی\n",
            "قوانین موجود کامپیوتر دانشجویات ارشد منطق مسخره\n",
            "غذا قوانین رفتار مسئولین راضی\n",
            "رفتار اساتید ندید\n",
            "قوانین ورود خروج زمانی\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"number of sentences in whole document = {voc_Doc.num_sentences}\")\n",
        "print(f'number of unique word in whole document = {voc_Doc.num_words}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikPr4q8VfYtT",
        "outputId": "7b222f46-1ca1-48b1-c7c5-36f2b7b79a3d"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of sentences in whole document = 231\n",
            "number of unique word in whole document = 583\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sortedL_doc=sorted(voc_Doc.word2count.items(), key=lambda kv:\n",
        "                 ( kv[1] , kv[0]))\n",
        "print(sortedL_doc)\n",
        "\n",
        "vocab=[ sortedL_doc[i][0]  for i in range(len(sortedL_doc)) ]\n",
        "vector=[ sortedL_doc[i][1]  for i in range(len(sortedL_doc)) ]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UVlVJXNfgLq",
        "outputId": "668509a3-0e89-406c-d02f-5e96eb014461"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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('نمیره', 1), ('نمیکند', 1), ('نمی\\u200cشود', 1), ('نمی\\u200cکن', 1), ('نمی\\u200cکند', 1), ('نمی\\u200cگذارند', 1), ('نگذریم', 1), ('نگهبانان', 1), ('نیست', 1), ('نیستم', 1), ('نیستند', 1), ('هاست', 1), ('هستن', 1), ('هسونه', 1), ('هفته\\u200cای', 1), ('همایش\\u200cهای', 1), ('همش', 1), ('همچنین', 1), ('هواش', 1), ('هواشم', 1), ('هوای', 1), ('واحد\\u200cهای', 1), ('وارد', 1), ('واقعاعملکرد', 1), ('وام\\u200cهای', 1), ('ودرسخوان', 1), ('وسایل', 1), ('وضعیه', 1), ('وعادلانه', 1), ('وعده', 1), ('وقتبره', 1), ('وقتی', 1), ('پای', 1), ('پاین', 1), ('پاینه', 1), ('پدرمون', 1), ('پر', 1), ('پراکنده', 1), ('پروژه\\u200cهای', 1), ('پروژکتور\\u200cها', 1), ('پروژکتور\\u200cهای', 1), ('پنج', 1), ('پنجشنبه\\u200cها', 1), ('پوژه\\u200cهای', 1), ('پیتزاش', 1), ('پیشرفته', 1), ('کاراموزی', 1), ('کارکردن', 1), ('کارکن', 1), ('کارکنان', 1), ('کافی', 1), ('کالا', 1), ('کاملا', 1), ('کتاب', 1), ('کلاسهای', 1), ('کمبود', 1), ('کمتر', 1), ('کمیت', 1), ('کنار', 1), ('کند', 1), ('کنید', 1), ('کیفی', 1), ('کیفیتی', 1), ('گرایش\\u200cها', 1), ('گوش', 1), ('گیرن', 1), ('یا', 1), ('یاد', 1), ('یخ', 1), ('آموزشی', 2), ('احترام', 2), ('ارائه', 2), ('ازمایشگاه', 2), ('اساتیدی', 2), ('اساس', 2), ('استاد', 2), ('استادا', 2), ('استرس', 2), ('اطلاع', 2), ('امتحان\\u200cها', 2), ('اموزشی', 2), ('انجام', 2), ('اکثر', 2), ('باعث', 2), ('بالاس', 2), ('برخی', 2), ('بری', 2), ('بزرگی', 2), ('بسته', 2), ('بندی', 2), ('تحصیل', 2), ('تحقیق', 2), ('تلاش', 2), ('تمرین', 2), ('حجم', 2), ('حق', 2), ('حل', 2), ('خابگاه\\u200cها', 2), ('خدا', 2), ('خودشون', 2), ('دانشگاه\\u200cها', 2), ('درس\\u200cها', 2), ('دروس', 2), ('دلسوز', 2), ('دلیل', 2), ('دم', 2), ('دوست', 2), ('دوستانه', 2), ('راه', 2), ('رسانی', 2), ('رشته', 2), ('سختگیرانه', 2), ('سختگیری', 2), ('سر', 2), ('سرویس', 2), ('سمینار', 2), ('سه', 2), ('شهید', 2), ('صمیمی', 2), ('صنعت', 2), ('صنعتی', 2), ('عالی', 2), ('علمی', 2), ('غذا\\u200cهای', 2), ('فارغ', 2), ('فکر', 2), ('فکری', 2), ('لحاظ', 2), ('مدیریت', 2), ('مسئول', 2), ('مسئولیت', 2), ('مسیر\\u200cهای', 2), ('مشکل', 2), ('مضخرفه', 2), ('مطالب', 2), ('مناسبی', 2), ('منطق', 2), ('مهم', 2), ('موجود', 2), ('میشود', 2), ('ناراضی', 2), ('ندید', 2), ('نظرات', 2), ('نظمی', 2), ('نمره', 2), ('نیستن', 2), ('ورزشی', 2), ('وقت', 2), ('پایان', 2), ('پذیر', 2), ('ک', 2), ('کار', 2), ('کاری', 2), ('کرونا', 2), ('کسی', 2), ('کل', 2), ('کلاس', 2), ('کمک', 2), ('کیفیتشون', 2), ('گیر', 2), ('یکی', 2), ('ازمایشگاه\\u200cهای', 3), ('استادان', 3), ('افتضاح', 3), ('امتحانات', 3), ('اندازه', 3), ('ایجاد', 3), ('برخورد', 3), ('بعد', 3), ('بیشتر', 3), ('تمام', 3), ('جای', 3), ('خوش', 3), ('دانشجو\\u200cهای', 3), ('دسترسی', 3), ('دیگه', 3), ('رابطه', 3), ('روش\\u200cهای', 3), ('زمان', 3), ('سطح', 3), ('شبانه', 3), ('غلط', 3), ('غیر', 3), ('قیمت', 3), ('مترو', 3), ('محل', 3), ('مختلف', 3), ('نحوه', 3), ('نداره', 3), ('نمرات', 3), ('نمی\\u200cدهند', 3), ('هیچ', 3), ('ورود', 3), ('پردیس', 3), ('کردم', 3), ('ی', 3), ('اموزش', 4), ('اهمیت', 4), ('بده', 4), ('بسیار', 4), ('تابستان', 4), ('تبعیض', 4), ('رفتار', 4), ('ضعیف', 4), ('مجازی', 4), ('محور', 4), ('موضوع', 4), ('می\\u200cدهند', 4), ('می\\u200cشود', 4), ('کامپیوتر', 4), ('کلاس\\u200cها', 4), ('یک', 4), ('ارتباط', 5), ('خوابگاه', 5), ('خوبی', 5), ('داخل', 5), ('دوره', 5), ('روز', 5), ('زیادی', 5), ('سخته', 5), ('عالیه', 5), ('مسیر', 5), ('اذیت', 6), ('ارزیابی', 6), ('ارشد', 6), ('بوفه', 6), ('تعداد', 6), ('جا', 6), ('سخت', 6), ('شیب', 6), ('مسئولین', 6), ('انتخاب', 7), ('می\\u200cکند', 7), ('کیفیت', 7), ('تنوع', 8), ('غذای', 8), ('آموزش', 9), ('فضای', 9), ('امکانات', 10), ('سلف', 10), ('دانشجو\\u200cها', 11), ('قوانین', 11), ('دانشجو', 12), ('مناسب', 14), ('دانشجویان', 15), ('راضی', 15), ('غذا', 15), ('اساتید', 40)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**select negative comments and count vocabularies of it**"
      ],
      "metadata": {
        "id": "fT63k8an2pSF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neg_doc = df.loc[df['Label'] == 1]\n",
        "\n",
        "neg_doc.to_csv(r'c:\\data\\negative.txt', header=None, index=None, sep='\\t', mode='a')\n",
        "\n",
        "neg_Doc_v = Vocabulary('neg_doc_v')\n",
        "\n",
        "for x in neg_doc.index:\n",
        "    #print(x)\n",
        "    text = neg_doc.loc[x, \"Comment\"]\n",
        "    #print(text)\n",
        "    neg_Doc_v.add_sentence(text)\n",
        "neg_doc.head(10)\n",
        "\n",
        "print(f\"number of sentences in whole documents = {neg_Doc_v.num_sentences}\")\n",
        "print(f'number of unique word in whole documents = {neg_Doc_v.num_words}')\n",
        "\n",
        "\n",
        "sortedL_neg_Doc=sorted(neg_Doc_v.word2count.items(), key=lambda kv:\n",
        "                 ( kv[1] , kv[0]))\n",
        "\n",
        "\n",
        "vocab_neg=[ sortedL_neg_Doc[i][0]  for i in range(len(sortedL_neg_Doc)) ]\n",
        "vector_neg=[ sortedL_neg_Doc[i][1]  for i in range(len(sortedL_neg_Doc)) ]\n",
        "print(sortedL_neg_Doc)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbUMod_DzXry",
        "outputId": "0a6dd97a-09d3-4c4e-b51d-dc2fa34687e9"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of sentences in whole documents = 145\n",
            "number of unique word in whole documents = 422\n",
            "[('آدم', 1), ('آشغاله', 1), ('آمادگی', 1), ('اب', 1), ('احترام', 1), ('اخرش', 1), ('اخیرا', 1), ('اداری', 1), ('ادم', 1), ('ارتیاط', 1), ('ازار', 1), ('ازمایشگاه', 1), ('اساتیدبه', 1), ('اساس', 1), ('استاد', 1), ('استادی', 1), ('استاد\\u200cها', 1), ('استاد\\u200cهای', 1), ('اشغال', 1), ('اصلی', 1), ('اطلاع', 1), ('اعتراض', 1), ('افت', 1), ('افتضاحن', 1), ('افتضاحه', 1), ('افتضاحی', 1), ('افسردگی', 1), ('التحصیل', 1), ('الکی', 1), ('امتح', 1), ('امتحان', 1), ('امتحانا', 1), ('امور', 1), ('امکاناتی', 1), ('انتقادات', 1), ('اندازه', 1), ('انعطاف', 1), ('اهمیتی', 1), ('اولی', 1), ('اومدم', 1), ('اون', 1), ('ایراد', 1), ('اینتر', 1), ('اینترنت', 1), ('اینطورین', 1), ('اینقدر', 1), ('اینهمه', 1), ('بارم', 1), ('باعث', 1), ('بخوایم', 1), ('بدردنخور', 1), ('بدن', 1), ('بدی', 1), ('برخلاف', 1), ('برنامگی', 1), ('بروکراسی', 1), ('برگزاری', 1), ('برگشتنش', 1), ('بریم', 1), ('بستر', 1), ('بسیار', 1), ('بعد', 1), ('بعضیا', 1), ('بقیش', 1), ('بقیه', 1), ('بله', 1), ('بوفه', 1), ('بچه\\u200cها', 1), ('بکشن', 1), ('بگذرانیم', 1), ('بگم', 1), ('بیان', 1), ('بیتوجهی', 1), ('بیخود', 1), ('بیدار', 1), ('بیشتر', 1), ('بیکار', 1), ('تاحالا', 1), ('تاسف', 1), ('تحمیل', 1), ('تدریس', 1), ('ترم', 1), ('تسهیلات', 1), ('تصوراتم', 1), ('تلف', 1), ('تمام', 1), ('تمرکز', 1), ('تند', 1), ('تنوعی', 1), ('توجهی', 1), ('توزیع', 1), ('توی', 1), ('جابجای', 1), ('جاش', 1), ('جایگاه', 1), ('جز', 1), ('جزی', 1), ('حال', 1), ('حداقل', 1), ('حذف', 1), ('حرف', 1), ('حفظی', 1), ('حقوق', 1), ('حمل', 1), ('حوزه\\u200cی', 1), ('خانه', 1), ('خجالت', 1), ('خدمات', 1), ('خراب', 1), ('خسته', 1), ('خودشون', 1), ('خوش', 1), ('خوندن', 1), ('خونهاینا', 1), ('دا', 1), ('داتشجو', 1), ('دادن', 1), ('دانشجویات', 1), ('دانشجو\\u200cهای', 1), ('دانشکد', 1), ('دانشگاه\\u200cها', 1), ('دانشگاه\\u200cهای', 1), ('دانگاه', 1), ('درامده', 1), ('درد', 1), ('درس', 1), ('درست', 1), ('درستی', 1), ('درس\\u200cهای', 1), ('درک', 1), ('دلخواه', 1), ('دم', 1), ('دنبال', 1), ('دهنده', 1), ('دهی', 1), ('دوران', 1), ('دوری', 1), ('دوست', 1), ('دوچرخه', 1), ('دچرخه', 1), ('دیربرگشتن', 1), ('ذره', 1), ('رابطه', 1), ('راهنما', 1), ('رسانی', 1), ('رسیدگی', 1), ('رشته', 1), ('رضایتی', 1), ('رفاه', 1), ('رفاهی', 1), ('رفتار', 1), ('رفتن', 1), ('رها', 1), ('روان', 1), ('روح', 1), ('روش', 1), ('روی', 1), ('زده', 1), ('زمانبندی', 1), ('زمین', 1), ('ساختمانها', 1), ('ساعت', 1), ('ساعت\\u200cهای', 1), ('سال', 1), ('سختگیری\\u200cهای', 1), ('سختی', 1), ('سر', 1), ('سراشیبی', 1), ('سرعت', 1), ('سرویسی', 1), ('سلب', 1), ('سلف\\u200cها', 1), ('سوالات', 1), ('سگ\\u200cهای', 1), ('سیستم', 1), ('شاهد', 1), ('شدت', 1), ('شده\\u200cام', 1), ('شرایط', 1), ('شرم', 1), ('شنیدن', 1), ('شیب\\u200cهای', 1), ('صبح', 1), ('صف', 1), ('ضر', 1), ('طوری', 1), ('طولان', 1), ('طولانی', 1), ('عادلانه', 1), ('عجیب', 1), ('عدالت', 1), ('عذاها', 1), ('عملکرد', 1), ('غذارو', 1), ('غذاهای', 1), ('غذا\\u200cهای', 1), ('غیرموثر', 1), ('فارغ', 1), ('فایده', 1), ('فضای', 1), ('قائل', 1), ('قاطع', 1), ('قاعل', 1), ('قبل', 1), ('قرار', 1), ('لابی', 1), ('لطفی', 1), ('لعنتشون', 1), ('ماسک', 1), ('مجبور', 1), ('محل', 1), ('محیط', 1), ('مخالفت', 1), ('مختلف', 1), ('مدرک', 1), ('مزخرفه', 1), ('مزخرف\\u200cترین', 1), ('مسئول', 1), ('مسئولان', 1), ('مسئولیت', 1), ('مسخره', 1), ('مسخره\\u200cای', 1), ('مسخره\\u200cترین', 1), ('مسعله', 1), ('مسیر\\u200cها', 1), ('ملکت', 1), ('مناسبی', 1), ('منصفانه', 1), ('منطقه', 1), ('میتونم', 1), ('میخوای', 1), ('میخوره', 1), ('میدهند', 1), ('میشوند', 1), ('میگذارند', 1), ('می\\u200cدهند', 1), ('می\\u200cش', 1), ('می\\u200cشود', 1), ('می\\u200cشوند', 1), ('می\\u200cگیره', 1), ('نارضایتی', 1), ('ناعادلانه', 1), ('نامه', 1), ('نامه\\u200cها', 1), ('نبود', 1), ('نبودن', 1), ('نت', 1), ('ندار', 1), ('ندید', 1), ('نشستن', 1), ('نظرسنجی\\u200cها', 1), ('نظرسنجی\\u200cهای', 1), ('نظم', 1), ('نفر', 1), ('نفرشون', 1), ('نقشهی', 1), ('نقل', 1), ('نقلیه', 1), ('نمیره', 1), ('نمیکند', 1), ('نمی\\u200cشود', 1), ('نمی\\u200cکن', 1), ('نمی\\u200cگذارند', 1), ('نیست', 1), ('نیستم', 1), ('نیستند', 1), ('هاست', 1), ('هفته\\u200cای', 1), ('همش', 1), ('همچنین', 1), ('هوای', 1), ('واحد\\u200cهای', 1), ('وارد', 1), ('واقعاعملکرد', 1), ('وسایل', 1), ('وضعیه', 1), ('وعادلانه', 1), ('وعده', 1), ('وقتبره', 1), ('وقتی', 1), ('پای', 1), ('پاین', 1), ('پاینه', 1), ('پدرمون', 1), ('پذیر', 1), ('پراکنده', 1), ('پروژکتور\\u200cها', 1), ('پنج', 1), ('ک', 1), ('کارکردن', 1), ('کالا', 1), ('کاملا', 1), ('کتاب', 1), ('کل', 1), ('کلاسهای', 1), ('کلاس\\u200cها', 1), ('کمبود', 1), ('کمتر', 1), ('کمیت', 1), ('کنار', 1), ('کند', 1), ('کنید', 1), ('کیفی', 1), ('کیفیتشون', 1), ('کیفیتی', 1), ('گوش', 1), ('گیر', 1), ('گیرن', 1), ('ی', 1), ('یا', 1), ('یاد', 1), ('یخ', 1), ('ارائه', 2), ('ارتباط', 2), ('استادا', 2), ('استرس', 2), ('امتحان\\u200cها', 2), ('اموزشی', 2), ('اهمیت', 2), ('اکثر', 2), ('ایجاد', 2), ('برخی', 2), ('بری', 2), ('بسته', 2), ('بندی', 2), ('تمرین', 2), ('حجم', 2), ('حل', 2), ('خابگاه\\u200cها', 2), ('خدا', 2), ('درس\\u200cها', 2), ('دروس', 2), ('دسترسی', 2), ('دلیل', 2), ('راه', 2), ('روز', 2), ('روش\\u200cهای', 2), ('زمان', 2), ('سختگیرانه', 2), ('سختگیری', 2), ('سرویس', 2), ('سمینار', 2), ('سه', 2), ('صمیمی', 2), ('فکر', 2), ('فکری', 2), ('قیمت', 2), ('مسیر\\u200cهای', 2), ('مشکل', 2), ('مضخرفه', 2), ('مطالب', 2), ('منطق', 2), ('مهم', 2), ('موجود', 2), ('موضوع', 2), ('میشود', 2), ('ناراضی', 2), ('نظرات', 2), ('نظمی', 2), ('نمرات', 2), ('نمره', 2), ('نیستن', 2), ('وقت', 2), ('پایان', 2), ('کار', 2), ('کرونا', 2), ('کسی', 2), ('کلاس', 2), ('یک', 2), ('استادان', 3), ('افتضاح', 3), ('امتحانات', 3), ('اموزش', 3), ('انتخاب', 3), ('تنوع', 3), ('جای', 3), ('دیگه', 3), ('غلط', 3), ('غیر', 3), ('مترو', 3), ('مجازی', 3), ('نحوه', 3), ('نداره', 3), ('نمی\\u200cدهند', 3), ('هیچ', 3), ('بده', 4), ('تابستان', 4), ('تبعیض', 4), ('تعداد', 4), ('جا', 4), ('زیادی', 4), ('سخته', 4), ('ضعیف', 4), ('محور', 4), ('مسئولین', 4), ('مناسب', 4), ('کامپیوتر', 4), ('اذیت', 5), ('ارزیابی', 5), ('ارشد', 5), ('امکانات', 5), ('خوابگاه', 5), ('داخل', 5), ('دوره', 5), ('مسیر', 5), ('می\\u200cکند', 5), ('آموزش', 6), ('سخت', 6), ('شیب', 6), ('کیفیت', 6), ('غذای', 7), ('دانشجو', 8), ('دانشجویان', 8), ('سلف', 9), ('دانشجو\\u200cها', 10), ('راضی', 10), ('قوانین', 10), ('غذا', 13), ('اساتید', 19)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**select posetive comments and count vocabularis of it**"
      ],
      "metadata": {
        "id": "N8kBz-op2099"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pos_doc = df.loc[df['Label'] == 3]\n",
        "pos_doc.to_csv(r'c:\\data\\posetive.txt', header=None, index=None, sep='\\t', mode='a')\n",
        "df.to_csv(r'c:\\data\\df.txt', header=None, index=None, sep='\\t', mode='a')\n",
        "pos_Doc_v = Vocabulary('pos_doc_v')\n",
        "\n",
        "for x in pos_doc.index:\n",
        "    #print(x)\n",
        "    text = pos_doc.loc[x, \"Comment\"]\n",
        "    #print(text)\n",
        "    pos_Doc_v.add_sentence(text)\n",
        "pos_doc.head(10)\n",
        "\n",
        "\n",
        "\n",
        "print(f\"number of sentences in whole document = {pos_Doc_v.num_sentences}\")\n",
        "print(f'number of unique word in whole document = {pos_Doc_v.num_words}')\n",
        "\n",
        "\n",
        "\n",
        "sortedL_pos_Doc=sorted(pos_Doc_v.word2count.items(), key=lambda kv:\n",
        "                 ( kv[1] , kv[0]))\n",
        "\n",
        "\n",
        "vocab_pos=[ sortedL_pos_Doc[i][0]  for i in range(len(sortedL_pos_Doc)) ]\n",
        "vector_pos=[ sortedL_pos_Doc[i][1]  for i in range(len(sortedL_pos_Doc)) ]\n",
        "print(sortedL_pos_Doc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHTcHtWVzpSb",
        "outputId": "e3bd3746-9f2c-42ee-884c-7193c9837fff"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of sentences in whole document = 85\n",
            "number of unique word in whole document = 239\n",
            "[('آب', 1), ('آشنا', 1), ('اجازه', 1), ('احترام', 1), ('اختصاص', 1), ('اخلاق', 1), ('ادب', 1), ('اذیت', 1), ('ارتباطی', 1), ('ارزیابی', 1), ('ارشد', 1), ('ازمایشگاه', 1), ('ازمایشگاهیش', 1), ('ازمایشگاه\\u200cها', 1), ('اساتیدکمک', 1), ('اساس', 1), ('استاد', 1), ('استادام', 1), ('اطلاع', 1), ('التحصیلی', 1), ('العادست', 1), ('اموزش', 1), ('امکان', 1), ('انشجو', 1), ('اول', 1), ('ایجاد', 1), ('باسواد', 1), ('باشه', 1), ('باعث', 1), ('باغچه\\u200cها', 1), ('باه', 1), ('بدک', 1), ('برا', 1), ('برق', 1), ('برقراری', 1), ('برگزار', 1), ('بزرگ', 1), ('بزرگه', 1), ('بستگی', 1), ('بهتره', 1), ('بهترین', 1), ('بودنشو', 1), ('بوفه\\u200cها', 1), ('بپردازد', 1), ('تجربه\\u200cهای', 1), ('تحقیقات', 1), ('تخصی', 1), ('ترجیحش', 1), ('تعریف', 1), ('تعطیل', 1), ('تفاوت', 1), ('تمیزه', 1), ('تهران', 1), ('توجه', 1), ('تکنولوژیهای', 1), ('جوان', 1), ('حراست', 1), ('حضور', 1), ('حقیقتا', 1), ('حوزه', 1), ('خاصی', 1), ('خودشون', 1), ('خی', 1), ('داریم', 1), ('دانش', 1), ('دانشجوی', 1), ('دانشجو\\u200cها', 1), ('دانشگاهای', 1), ('دانشگاه\\u200cها', 1), ('دسترسی', 1), ('دقیقا', 1), ('دلسوزی', 1), ('دم', 1), ('دوست', 1), ('دوستی', 1), ('دکتری', 1), ('رابطه\\u200cهای', 1), ('راحت\\u200cتر', 1), ('راه\\u200cهای', 1), ('رسانی', 1), ('رستوران', 1), ('رشته', 1), ('رشته\\u200cهای', 1), ('رضایت', 1), ('رقابت', 1), ('روش\\u200cهای', 1), ('زمان', 1), ('زمان\\u200cهای', 1), ('زیادی', 1), ('زیبا', 1), ('سالم', 1), ('سبز', 1), ('سخته', 1), ('سر', 1), ('سرسبز', 1), ('سرسبزه', 1), ('سلف', 1), ('شرایطم', 1), ('شرکت\\u200cها', 1), ('طبیعت', 1), ('طریقه', 1), ('عرض', 1), ('علاقه', 1), ('عمل', 1), ('غذای', 1), ('غذا\\u200cها', 1), ('غذا\\u200cهای', 1), ('فارغ', 1), ('فراهم', 1), ('فراوان', 1), ('فرهنگی', 1), ('فوق', 1), ('فیلدها', 1), ('قایل', 1), ('قسمت\\u200cها', 1), ('قشنگی', 1), ('قیمت', 1), ('لوکس', 1), ('متفاوت', 1), ('متنوع', 1), ('مجازی', 1), ('محوطه', 1), ('مرکزی', 1), ('مسئول', 1), ('مسئولیت', 1), ('مسائل', 1), ('مفیدی', 1), ('مناسبی', 1), ('منم', 1), ('مهربان', 1), ('مواقع', 1), ('مورد', 1), ('موضوعات', 1), ('مید', 1), ('می\\u200cباشد', 1), ('می\\u200cرسند', 1), ('می\\u200cکنه', 1), ('ندید', 1), ('نسبت', 1), ('نمرات', 1), ('نمیدهند', 1), ('نمی\\u200cکند', 1), ('نگذریم', 1), ('نگهبانان', 1), ('هستن', 1), ('هسونه', 1), ('همایش\\u200cهای', 1), ('هواش', 1), ('هواشم', 1), ('وام\\u200cهای', 1), ('ودرسخوان', 1), ('پذیر', 1), ('پر', 1), ('پروژه\\u200cهای', 1), ('پروژکتور\\u200cهای', 1), ('پنجشنبه\\u200cها', 1), ('پوژه\\u200cهای', 1), ('پیتزاش', 1), ('پیشرفته', 1), ('ک', 1), ('کاراموزی', 1), ('کارکن', 1), ('کارکنان', 1), ('کافی', 1), ('کل', 1), ('کیفیت', 1), ('کیفیتشون', 1), ('گرایش\\u200cها', 1), ('گیر', 1), ('آموزشی', 2), ('اساتیدی', 2), ('انجام', 2), ('اندازه', 2), ('اهمیت', 2), ('بالاس', 2), ('بزرگی', 2), ('بعد', 2), ('بیشتر', 2), ('تحصیل', 2), ('تحقیق', 2), ('تعداد', 2), ('تلاش', 2), ('تمام', 2), ('جا', 2), ('حق', 2), ('خوش', 2), ('دانشجو\\u200cهای', 2), ('دلسوز', 2), ('دوستانه', 2), ('رابطه', 2), ('شهید', 2), ('صنعت', 2), ('صنعتی', 2), ('عالی', 2), ('علمی', 2), ('غذا', 2), ('لحاظ', 2), ('محل', 2), ('مختلف', 2), ('مدیریت', 2), ('مسئولین', 2), ('موضوع', 2), ('می\\u200cکند', 2), ('ورزشی', 2), ('ورود', 2), ('کاری', 2), ('کمک', 2), ('ی', 2), ('یک', 2), ('یکی', 2), ('آموزش', 3), ('ارتباط', 3), ('ازمایشگاه\\u200cهای', 3), ('برخورد', 3), ('بسیار', 3), ('رفتار', 3), ('روز', 3), ('سطح', 3), ('شبانه', 3), ('می\\u200cدهند', 3), ('می\\u200cشود', 3), ('پردیس', 3), ('کردم', 3), ('کلاس\\u200cها', 3), ('انتخاب', 4), ('دانشجو', 4), ('امکانات', 5), ('بوفه', 5), ('تنوع', 5), ('خوبی', 5), ('راضی', 5), ('عالیه', 5), ('دانشجویان', 7), ('فضای', 8), ('مناسب', 10), ('اساتید', 21)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Counting number of unic words in whole dataset, negative and posetive data**"
      ],
      "metadata": {
        "id": "JajuvF1JcGfn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "N_Fre_w = vocab[560:]\n",
        "new_df = pd.DataFrame(dict([  (w,[0,0,0 ])  for w in N_Fre_w ]))\n",
        "new_df.iloc[0]=[ voc_Doc.word2count[i]  for i in new_df.columns.values.tolist()]\n"
      ],
      "metadata": {
        "id": "SnJ86CwS4HdV"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import append\n",
        "\n",
        "ll=[]\n",
        "for i in new_df.columns.values.tolist():\n",
        "\n",
        "  if i not in pos_Doc_v.word2count.keys():\n",
        "    ll.append(0)\n",
        "  else:\n",
        "    ll.append(pos_Doc_v.word2count[i])\n",
        "\n",
        "\n",
        "new_df.iloc[1]=ll"
      ],
      "metadata": {
        "id": "OpU6YRJ6ZQy_"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import append\n",
        "\n",
        "ll=[]\n",
        "for i in new_df.columns.values.tolist():\n",
        "\n",
        "  if i not in neg_Doc_v.word2count.keys():\n",
        "    ll.append(0)\n",
        "  else:\n",
        "    ll.append(neg_Doc_v.word2count[i])\n",
        "\n",
        "\n",
        "new_df.iloc[2]=ll"
      ],
      "metadata": {
        "id": "gFa9g60GZXHN"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "a7pAQfT_FpQz",
        "outputId": "6cf8072e-4933-4b36-879a-c693f3fac9ff"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   سخت  شیب  مسئولین  انتخاب  می‌کند  کیفیت  تنوع  غذای  آموزش  فضای  امکانات  \\\n",
              "0    6    6        6       7       7      7     8     8      9     9       10   \n",
              "1    0    0        2       4       2      1     5     1      3     8        5   \n",
              "2    6    6        4       3       5      6     3     7      6     1        5   \n",
              "\n",
              "   سلف  دانشجو‌ها  قوانین  دانشجو  مناسب  دانشجویان  راضی  غذا  اساتید  \n",
              "0   10         11      11      12     14         15    15   15      40  \n",
              "1    1          1       0       4     10          7     5    2      21  \n",
              "2    9         10      10       8      4          8    10   13      19  "
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Plot distribution of each word in posetive and negative data**"
      ],
      "metadata": {
        "id": "l3hDfUfacg94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnt=dict(new_df.loc[1:,\"غذا\"])"
      ],
      "metadata": {
        "id": "Zy0-btuJRIIG"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = list(cnt.keys())\n",
        "sizes = list(cnt.values())\n",
        "colors = ['#3fba36', '#66b3ff','#ffcc99','#ff9999', '#d44444']\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, labels=labels, colors=colors,\n",
        "        autopct='%1.1f%%', startangle=90)\n",
        "#draw circle\n",
        "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
        "fig = plt.gcf()\n",
        "fig.gca().add_artist(centre_circle)\n",
        "# Equal aspect ratio ensures that pie is drawn as a circle\n",
        "ax1.axis('equal')  \n",
        "plt.tight_layout()\n",
        "# Decomment following line if you want to save the figure\n",
        "# plt.savefig('distribution.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "8xbhlt0iRdak",
        "outputId": "0cc39294-176e-4ea7-f1b1-a781577331e7"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in new_df.columns.values.tolist():\n",
        "  print(i)\n",
        "  cnt=dict(new_df.loc[1:,i])\n",
        "  labels = list(cnt.keys())\n",
        "  sizes = list(cnt.values())\n",
        "  colors = ['#3fba36', '#66b3ff','#ffcc99','#ff9999', '#d44444']\n",
        "  fig1, ax1 = plt.subplots()\n",
        "  ax1.pie(sizes, labels=labels, colors=colors,\n",
        "          autopct='%1.1f%%', startangle=90)\n",
        "  #draw circle\n",
        "  centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
        "  fig = plt.gcf()\n",
        "  fig.gca().add_artist(centre_circle)\n",
        "  # Equal aspect ratio ensures that pie is drawn as a circle\n",
        "  ax1.axis('equal')  \n",
        "  plt.tight_layout()\n",
        "  # Decomment following line if you want to save the figure\n",
        "  # plt.savefig('distribution.png')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "E0F2ZatEQbpG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "028824ca-834c-4463-be86-87ea35d5cbf6"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "سخت\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "شیب\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "مسئولین\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "انتخاب\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "می‌کند\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "کیفیت\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "تنوع\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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u1P9YBxAVlDk/7h0MOHkL42n7/y/5efnWMSRLFRXChEHWKXbp5LJydAKnMRWUPSc3l+wS7sLx/U+wjiFZ7hh3p/l6A5+zDpHrVFD2nCyoSP8T6JbfzTqGZLlBRXBIH+sUu+Tk1HsuUUEZ8uNeV8C3ztGSaYNOtY4gOcLhUZRO4zSmgrJ1LNDZOsSORvYq5aAeh1jHEAfU1tYyfvx4Ro0axWGHHca1114LwNFHH01paSmlpaUMGDCAadNaHmyEw+FtH3fyyZ8uuDnrrLMYOXIkV199NaP3hR4FMPeP17Ns9mMZeV2tdEhZOQOtQ+QyXQG35eRFnmn76T5FSSksLOS5556je/fu1NfXM2nSJE444QRmzpy57WNOPfVUTjnllBY/v0uXLsyfP3+7P1uwYAFdunRhwYIFRCIRNm9az/DOm/nT25WMOf3/OvT1tIMPPGQdIldpBGVrsnWAHfUu6MPR/XzrGOKIUChE9+6pDfbr6+upr68nddJ5yoYNG3juued2OYJqSadOnaipqaGxsZH6+nrC4TDP3/99xp35w7TnTwPfOkAuU0EZ8eNeb8C5XcmmDJhKpzytrpVPJRIJSktL6devH5FIBM/79ESKxx57jOOOO46ioqIWP7e2tpZx48YxYcIEHnssNX03bNgwSkpKGDNmDCeddBLvvPMOBXmN+EeNycjraSPfOkAu0xSfnQk4uDns0SW+dQRxTDgcZv78+axbt47p06ezcOFCDj/8cAAeeeQRZsyYscvPXb58OQMHDmTp0qUce+yxjBgxgoMPPpjbbrtt28ecdNJJxGIxrr31Bp6b/SoDR0cY9vnzOvx1tdIhZeUMjEX5wDpILtIIyo5zJ//1LujD0J7uH9gjNnr16sXkyZN56qmnAFizZg1z5sxh6tSpu/ycgQNTawwOOuggfN9n3rx52/39448/ztixY9m0aRM1q5bwuSsf5d0X/0xD7eaOeyFt51sHyFUqKDvOFdSRfSeSF9K3hHxq9erVrFu3DoCamhri8ThDh6Zmpv/85z8TjUbp3LnlhaiffPIJdXV1QKrMXnzxRYYP//QfQPX19dx2221cfvnl1NTUUJgf4sBekGxMkGjY0sGvrE186wC5Sj+NDPhxLwyMt86xo6NKPmsdQRyzcuVKJk+ezMiRIzniiCOIRCJEo1EA/vCHP3DGGWds9/Evv/zytim/N998k3HjxjFq1CgmT57MlVdeuV1B3XnnnZxzzjl07dqVkSNHsnnzZmLnjqDvIWMp7N4rcy9yz46wDpCrQslk0jpDzvHjXikwb48fmEEFeYU87j+tncvF1IoN8KMXrFPspA7oHovSYB0k12gEZcO55Upj+xyhchJzg4qg2L1vw0LgUOsQuUgFZWOYdYAdTSw52jqCCAAj97FO0KLDrQPkIhWUDecKalyxt+cPEsmAw0qsE7TIyROvs50KyoZTBdWrU2/27dLfOoYIAAc4tT5iG42gDKigMsyPe12AA61zNDe4yLkNLSSHFRVCL+e2UFZBWVBBZd4QHPvvPkQFJY45oKd1gp0cXFaOe8s3spxTPyhzhFPTe6ARlLhnf/cKKg8H37vZTgWVeYOtA+xoSJHed+IWR69D7WcdINeooDJvf+sAzfXq1Jt+nd1c1yu5y8EpPgCtJMowFVTmDbIO0Jym98RFji6UUEFlmAoq85wqqIN1tLs4alDLR0xZGmAdINeooDJvX+sAzZUU9rOOINKi3hpB5TwVVAb5ca8T0Ns6R3PFhX2tI4i0qGehdYKdqKAyTAWVWf1w7BTdPoXF1hFEWlSkEVTOU0FllnO7jPUtdC6SCAC93BtB9SsrJ2wdIpeooDKrh3WAHRUXaAQlburp3ggqDOgNk0EqqMzqah2guR75RRSE3ftnqgg4WVAAbqbKUiqozHJqL69iXX8ShxUVOHbBNqXAOkAuUUFlllMFpQUS4rJwHnR3rw405ZBBKqjMcmqKrzBP7zVxWyf3liS4V5lZTAWVWU6NoMIh9979Is3luTfHp4LKIBVUZjlWUPnWEUR2K+xeQWnaIYNUUJnl1JBFIyhxXdi9n1AaQWWQe//7s1u9dYDmkiStI4jsVtK9b1GNoDJIBZVZW6wDNJdIJqwjiOxWwr2Ccm/SMYupoDKrzjpAcyoocV2jewW1yTpALlFBZZZGUCJtkGi0TrCTjdYBcokKKrOcKqhN9XqvidtqGqwT7EQjqAxSQWWWUwVVtWWNdQSRXaprgFoVVE5TQWVWjXWA5qrqqqwjiOzSeqeu2G6jaYcMUkFlllNDli2NdWys32AdQ6RF62utE7RII6gMUkFl1irrADvSKEpc5eAIanMsinvLNrKYCiqzHCwopwZ1Its4OILS6CnDVFAZVBGprAaqrXM0p4ISV61zbwSlN0uGqaAyz6lRlFbyiascHEEtsw6Qa1RQmedUQa2s+dA6gkiL1my2TrCT5dYBco0KKvM+tg7Q3OINb1tHENlJYxJWuLfAdJl1gFyjgsq8ZdYBmluyaTENje7dDSm57eNNUOfeTlwaQWWYCirzFlkHaG5L4xaWVb9rHUNkO++tt07QomXWAXKNCirznCoogEUb3rKOILKd5W4WlEZQGaaCyjwVlMgeODiCqsWx68e5QAWVee+T+mZ3xtsb3rSOILJNY9LJgloei+oI6kxTQWVYRaSyEVhinaM5LZQQlzi6QELTDAZUUDacmubb0riFdzY6FUly2LvrrBO0aL51gFykgrLxmnWAHf13zYvWEUQAWODmlR4VlAEVlI051gF29OLqmdYRRKhPwBurrVO0SAVlQAVlw7mCWrzxbRZ/tIjTTjuNoUOHMmzYMGbPns1ll13G0KFDGTlyJNOnT2fdupbnXw488EBGjBhBaWkp48aN2/bnV1xxBSNHjuTss8/e9mcPPvggt912W4e/Jgmet6ucvP70SSyqe6AsqKAMVEQqV+PgTX9fv/jrTJkyhbfeeotXX32VYcOGEYlEWLhwIQsWLGDw4MH85Cc/2eXnP//888yfP5+XX34ZgPXr1zN37lwWLFhAQUEBr732GjU1Ndx///1cdNFFmXpZEiCvujm995J1gFylgrLj1CiqobqBebPnce655wJQUFBAr169OP7448nPzwdgwoQJrFixotXPmZeXR319Pclkks2bN9OpUyduvvlmLrnkEjp16tQhr0OCzdHrT069V3OJCspOpXWA5mo/qiPZo5Evn/NlRo8ezYwZM6iu3v7oqvvuu48TTjihxc8PhUIcf/zxjB07lrvvvhuAHj16cOKJJzJ69Gj69+9Pz549qaysZNq0aR3+eiR43lsP65y6Q3AbFZQRFZQdp77pk4kkGxZvYuIZRzFv3jy6devGjTfeuO3vb7jhBvLz8znrrLNa/PxZs2Yxd+5cnnzySe68805eeOEFAC6//HLmz5/PLbfcwjXXXMN1113HPffcwxe/+EWuv/76jLw2CQZHp/fAsX9M5hIVlJ1XAGfODC3sW0BhSQEfD0xN4Z122mnMnTsXgAceeIDy8nIeeughQqFQi58/cOBAAPr168f06dOZM2f7/p03bx7JZJIhQ4bwpz/9iUcffZQlS5awePHiDnxVEiSvuHk02WuxqFtnuOUSFZSRikhlDTDLOsdWBX1SBfXUK09SVbeGZ599luHDh/PUU09x00038cQTT9C1a9cWP7e6upqNGzdu+/0zzzzD4Ycfvt3HXHPNNfzoRz+ivr6eRCK1TCsvL4/Nm907lU4y7+0qWLnJOkWLnrYOkMtUULac+uY/9KIDeP0niygdNZr58+dz9dVXc/HFF7Nx40YikQilpaWcf/75AHz44YeceOKJAHz88cdMmjSJUaNGMX78eKZOncqUKVO2Pe9jjz3GuHHjGDBgAL169aK0tJQRI0ZQW1vLqFGjTF6ruOXfy6wT7JJT79FcE0omtf+hFT/ujQRetc6xo5LCfvxh0t8I5+VbR5EcsK4Wrno2tUmsYzYDfWJRd6bic41GUIYqIpULgJXWOXa0um4V/1njzOyjZLlZ7zlZTgD/VjnZUkHZe8Y6QEsee/8v1hEkByQaYeZ71il2SdN7xlRQ9px8E7yydg7vVesAUelYCz529t4ncPS9mUtUUPaeAdzbfQx4XKMo6WAVy6wT7NJ7sajOgLKmgjJWEamsAp6zztGS8g8eY02dm1tLS/C9sxbeqrJOsUt/tw4gKihX/ME6QEvqGuv43dJ7rWNIlvrrm9YJdush6wCignLFX4Et1iFa8o8PnmDF5vetY0iWWfAxLPnEOsUuLYlFmW0dQlRQTqiIVK7D0QuyiWSCe9+5yzqGZJHGJPzN7as7Gj05QgXlDien+QCe//hfvL3B7Z8oEhyVH8CHG61T7JYKyhEqKHc8TurOdSfdvfhO6wiSBeoT8Pe3rVPs1kuxKIusQ0iKCsoRFZHKauAJ6xy78sraObxS5dQJIRJALyyHqhrrFLv1oHUA+ZQKyi2/sQ6wO3e8fStbGp1cyyEBsL4Wyt0+XaUBh6fac5EKyi3Pg7vTC+9WL9Wyc2m3B1+DzfXWKXbrGZ395BYVlEMqIpVJ4G7rHLvzyLLf8/YGt29gEff8d0VqabnjfmEdQLangkqzUCh0XygUWhUKhRa28ynuB5ydpU8kE9y48DpN9Umrra+FP75unWKPXo9F3dy4OZepoNLvAWDKnj5oVyoilWtxfJmrpvqkLQIwtQdwq3UA2ZkKKs2SyeQLwNq9fJrb05GlI2mqT1ojIFN7q9DqPSepoBzUdJChkxvIbqWpPtmTdcGY2gP4tQ4mdJMKyl3XWwfYk3erl/KLt262jiEOamiEe+YGYmqvFviVdQhpmQrKURWRyucB589d/8cHj/O39/5kHUMc84eFsHhvJ7oz4yEtLXeXCsptP7IO0Bp3LLqVV6peso4hjqhY5vQx7s0l0eIIp6mg0iwUCj0CzAaGhEKhFaFQ6Nz2PldFpPIZwPn9hRLJBD9YcDUfbF5hHUWMvbUmMNedAB6ORQlO2hykgkqzZDJ5RjKZ7J9MJjslk8lByWRyb9djB2IUtbFhA9+b/12qG6qto4iR1dVw9yup4zQCYAtwjXUI2T0VlOMqIpXlwDzrHK2xrPpdrn/t+ySSCesokmG1DfCrl6Ha/UURW8ViUd61DiG7p4IKhqusA7TW7DWzuGuR87dxSRo1NMJv5jp/xlNzmwjAKllRQQVCRaTyaeBJ6xyt9af3HuG+JU5vKShpkmiEe+fBwmCtg/u5Vu4FgwoqOL5D6jiAQPjd0nv5/dL7rWNIB2pMJnngVZi70jpJm6wGdPNeQKigAqIiUvkWcJd1jra4d8ldPLLs99YxpAMkkgl++1blosoPksFYEvGpG2JRgjMZmeNUUMHyA2CddYi2iC2+g98tvc86hqRRQ2MDP174A3674puDq8L3zUoSmJJ6B/i1dQhpPRVUgFREKquA66xztNV9S2L8ZrF2k8kG9Y31/PC17/HsR6mTKary7z46QCVVFouizSMDRAUVPHcAb1iHaKuHlv2Wn7/5UxoaA3MZTXawqX4jV8+/lJmrKrb781RJ3et6Sf02FnV7A2bZmQoqYCoilfXAuUCjdZa2emLFX/nu3G+wfkugZikFeK96ORfM+RovVVW2+PdV+b9xuaRWA5dah5C2U0EFUEWk8r/AL61ztMf8T17h/DlfZenGd6yjSCvNWTObC+d8jfc3736DPYdL6juxKFXWIaTtVFDB9T1gqXWI9lhZ8yEXvTSDWav+bR1F9uCPyx/iynnfYVPDplZ9vIMl9UwsqsMIg0oFFVAVkcrNwHnWOdqrJlHD/716Ob/XCj8nbUnUcePC6/j1ol/S2MbZZIdKajNwvnEG2QuhpPn3kOwNP+79BphhnWNv+Pscx6XDrqRHpyLrKAJ8VPMh1712DW+sX7hXz1PccN7M4sS5k0KEQmmK1laXx6L8zOhrSxpoBBV83wXetw6xNyo+fpavzj6T2atftI6S0xqTjTz+/l/46uwz97qcwHwk9Txwi8HXlTTSCCoL+HFvIlAB5BtH2WtT+k/loiHf0mgqw1bWfMjP3riBuWtfTvtzG4ykVgOjYlGCtQmT7EQFlSX8uHcV8GPrHOnQt7CES4ddxZElE62jZL3GZJTAn2gAAAgmSURBVCN/X/E37lp8OzWJmg77OsUNM2YVJ2ZMzEBJJYETYlGe7uCvIxmggsoSftwLAU8Bx1tnSZfP9z+Ri4d8W6OpDrKy5kNuev165n3ySka+XoZK6qZYlCs68Pklg1RQWcSPe/2A+UB/6yzp0qegD+ccNIOpA08hPy/wM5hOqG6o5tHlD/Ho8oc7dNTUkg4uqdnAZ2PR4Oz6L7ungsoyftybDPyLLFsAM7DLIM495Hz8fY4jL5RVLy1jtjRu4Yn3/8rv372f9fV2u3l0UEl9ApTGouz+bmIJFBVUFvLj3v8BP7LO0REO7TGE8w65kPF9J1hHCYxEMkF85VPcv+RuPq79yDoO0CElNS0W5fE0PZc4QgWVpfy49zBwhnWOjjK691jOO/RChvc83DqK015c/QL3LP4171a7t+lIGkvquliUa9MSSpyigspSftzrTOpekKweapT2Hsu0/U5lUskxukbVZHNDNf/66Gkee/8vLN3k9p6HaSiph2JRvpTWUOIMFVQW8+PePsAcYH/rLB2tT0ExJw2aRnTgNEo697OOY+LdTUt5YsVfePrDJ9mcqLaO02rFDefOKk6cd1SINl9cfAGI6Iyn7KWCynJ+3BsBvAj0sM6SCeFQmIkln+WUQV9gbPF46zgdrr6xnlmr/s3jK/7C/E/mWsdpt3aU1CLgyFiUtR2ZS2ypoHKAH/eiwONk2cq+PRnYZRCT+h3DkSWTGNFzJOEsmQKsS9Qyd+3LvLh6Jv9Z/QJrt2THz+g2lNQaYEIsypJM5BI7Kqgc4ce984C7rXNY6ZFfxISSiUwsOZojiifQLb+bdaQ2WVu3ltlrZjF79UxeqqqkrrHOOlKHaEVJ1QLHxqLMzmQusaGCyiF+3Ps28HPrHNbyQ/mU9h7L+L4TGFo0nEN7DKZLflfrWNtZv2U9iza+xVvrX2f2mhd5c/3rJMmN9+puSqoROCMW5VGLXJJ5Kqgc48e97wM/tM7hkhAh9ut2AEOKhjGkx1AGFw3NaGltLaNFG1KPtze8yUe1ub3PaQsllQRmxKLoALEcooLKQX7cuwm4zDqHy0KEGNB1EP0K+1Fc2HfnR0FfiguLKQx3JhwKt/gcicYGNic2U1VXRVXdaqrqqlhTt5q1W6qoqlvDmro1rKr9yJmbZ12zQ0mdH4sSs84kmaWCylF+3PsVcIF1jmwRDoXJD+WTBBLJBhLJhHWkrFDccO7Mvomv/ykW5XbrLJJ5ObWqS7ZzEXCPdYhskUgmqGusY0tjncopjary71U55TCNoHKcH/d+RupUXhGXJIELKyKVd1kHETsaQeW4ikjlZcDV1jlEmkkA56qcRCMoAcCPe+cDd6J/tIitauD0ikjlP6yDiD0VlGzjx73Tgd8DnayzSE5aBUytiFS+bB1E3KCCku34ce/zwKOAzlmXTFoEnFARqXTvXBAxo4KSnfhx7zCgHDjQOIrkhtnASRWRyirrIOIWXW+QnVREKl8HxpPaBV2kI/0ROE7lJC3RCEp2yY97BcDtwNets0jWaQAuq4hU3mYdRNylgpI98uNeGfBLoMA6i2SFj4D/qYhUzrIOIm5TQUmr+HHPAx4BPmOdRQJtJvDFikilNiCUPdI1KGmVikhlJVAKPGydRQLrNuBYlZO0lkZQ0mZ+3DsbuIMcOUZe9tpKYEZFpPKf1kEkWFRQ0i5+3DuY1JTfEdZZxGmPAhdURCqz41x6ySgVlLSbH/c6AdcCVwD5xnHELWuBiyoilX+wDiLBpYKSvebHvRHAbwDPOos44UlSm73m9rHAstdUUJIWftzLAy4EfoyuTeWqj0jd2/SgdRDJDiooSSs/7g0itYDiFOsskjENpP6fX1sRqdxgHUayhwpKOoQf96YBNwMHW2eRDjWT1LWm16yDSPZRQUmHadoq6QLgGqDYOI6k10pS03kPWQeR7KWCkg7nx72epE7t/QbQ2TiO7J21wE3A7RWRys3WYSS7qaAkY/y4tz9wPfAlIGQcR9pmI/Bz4Oe6ziSZooKSjPPj3nBS906die6fcl0NqQUQP9WRGJJpKigx48e9A4DvAucCXYzjyPbWAXcDt+l+JrGighJzftzrB3wTuAjoaRwn1y0GfgE8UBGprLYOI7lNBSXO8ONeD+DLpA5IHGUcJ9dUALcC5RWRykbjLCKACkoc5ce98UAZcDrQzThOtqoiteHvvRWRyvnWYUR2pIISp/lxrwg4CzgPGG0cJxvUk9or77ekRktbjPOI7JIKSgLDj3tDgNOA/0FTgG01F/gd8HBFpHK1dRiR1lBBSSD5ce9QPi0rjax21gi8CPwNeKwiUvmucR6RNlNBSeD5ce8g4Hjgc8BkoI9tIjNrgaeAfwJP6b4lCToVlGSVpmM/RgPHkSqsSWTvPVYrSW3WOhOYBSzQCjzJJiooyWp+3CsESoHxpI6nPwIYDORZ5mqHWuAt4BWaSqkiUrnUNpJIx1JBSc7x415XYCSp4hpO6kiQg4EDgUK7ZAAkgCXAQuC1pl8XAosrIpUJy2AimaaCEmnSND04kO0Lqy+pa1rFzR59aNu9WVuATaQ2XN0ErAc+BN4HVjQ9tv5+ZUWksmHvX41I8KmgRNqhaeqwEAiT2vA2vMOjkaZC0r1GIu2jghIREScF7UKxiIjkCBWUiIg4SQUlIiJOUkGJiIiTVFAiIuIkFZSIiDhJBSUiIk5SQYmIiJNUUCIi4iQVlIiIOEkFJSIiTlJBiYiIk1RQIiLiJBWUiIg4SQUlIiJOUkGJiIiTVFAiIuIkFZSIiDhJBSUiIk76f9Xf1L3qnFB3AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "غذای\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "آموزش\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "فضای\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "امکانات\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "سلف\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "دانشجو‌ها\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "قوانین\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "دانشجو\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "مناسب\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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44QRuvvlmysvLKSsrY/z48XzjG9/g/fffJ5lMMn78eEZshvkroNbt1e8uBZ62DiFf0AjKTRfh8CzdwV335ZtDL7KOIRmwbt06Jk2axNSpU+nevTt33XUXt912G0uWLOG2227jwgsv3OF7GhsbefHFF/n1r3/N7Nmz+eCDD7j//vsBmDp1KjU1NVx11VVce+213HDDDdx0001867yzKZ5zb5bvXZt9pbKafaxDyBdUUI6JxPwC4FvWOVqiQ3u5o6GhgUmTJnHuuedy5plnAvDHP/5x65/POuusnU6SGDRoEOPGjWPo0KEUFBRw+umn8/rrr3/p3zz++ONMmDCBdevWsXDhQh588EFe+dfDHFS6oePvWPvl4/ih9bBRQbmnAnB2cu6kfb6mQ3s5IJVKceGFF1JeXs6VV1659esDBgzg+eefB2D69OkMH77jmooTJ05kzZo1rFixYuu/GzVq1Nb/3tDQwNSpU5k8eTIbN27E89IHA5qamjh12Ga6FnbkPdtjF1VW43bCEFFBuedi6wAt6VbQjW8MdXZwJ23w8ssv8+c//5np06czbtw4xo0bx5NPPsm9997LVVddxdixY5kyZQr33HMPAK+++ioXXZQ+rJufn8+vf/1rjj/+eEaPHk0qleI73/liNuedd97JBRdcQJcuXRgzZgwbNmxg9OjRTJgwgYF9enKK2+sI9wH+zTqEpGnDQodEYv4+wCIcfePwnf0u5dx9L7COIQHX0ATXxWH1RuskLXqgqoJzrEOIoy+EIXYujv5OehWVcuY+Z1vHkBxQmA9f3d86xS59VStLuMHJF8MQm2QdoCXfHHaR1tqTjPEHwYAS6xQt6kL6QnkxpoJyRPPhvQnWOXZmUJe9OWXAV61jSA7J85zfJv4/rAOICsolZ1gHaMmFwy6hIE/XdEtmje0Hw/ayTtGiEyur3d/mJtepoNxxpnWAndmvZH+O6XucdQzJUWeMtE7QokJAhw2MqaAcEIn5ZcCR1jl25qx9ziHP08NEOsbwUtjH3e0CT7EOEHZ65XHDaTj4u+he2J1j+x5vHUNy3DGDrRO0KFpZrfVKLTn3ohhSTh7eO2XAV7WkkXS4QwZCsZs10AM4wjpEmKmgjEVifmfAuZM8Hh6nDnJ23obkkKJ8OHxv6xQt0mE+Qyooez7g3DDlkN6HMbCL+5v4SG442t3DfCooQyooe8dYB9iZ0wc5e82w5KB+3aC8t3WKnTqwshq9UzOigrJ3tHWA7fXr3J9Deh9mHUNC5pgh1gla5NxzNCxUUIYiMb8QcK4JThpwCvlevnUMCZkxfaBbkXWKnTrKOkBYqaBsTSC97pdTjixz8qij5Lj8PBjdxzrFTqmgjKigbDl36KCsUx+Gd3d7kTTJXWP7WifYqVGV1bh7OXEOU0HZcm6ocniZ3iyKnfIyKHDvVckjPdtWssy9h0K4OHf+6QgVlBjqXAAj3Fyi9VDrAGGkgjISifkDAafWci7O78K4Xk7u+CEh4uhhPj0xDKig7BxoHWB7E0t9ivLcnEYl4THGzYJy7vkaBiooOwdYB9iezj+JC/YqdnKF830rq92bcZvrVFB2nHtHNr7XwdYRRAAY6d6qEh4wyjpE2Kig7Dg1gtqrqBd9Ort5bEXCZ7B7Iyhw7DkbBiooA5GY79y7sf27u7u1qYSPg4f4wMGjHrlOBWVjMNDNOsS2RnQvt44gslWfrk7uEaURVJapoGw4NXoC2L9EIyhxi4OH+YZaBwgbFZQN57ZnG6FDfOKYfXpaJ9iBtt3IMhWUjQHWAba1V1Evyjq7uUqnhJeDI6iuldVuXVyf61RQNvpbB9iWzj+JixwsKHDw6EcuU0HZcKqgBnfd1zqCyA7Kujq5cKwKKovc+/WHg1MF1buTe1dFigD06GSdYAcqqCxSQdlw6hxUqQpKHNWjs3WCHQy0DhAmKqgsi8T8PMCpGQkqKHFVT/cKyr25hTlMBZV9fYB86xDbUkGJqxw8xOfm1I0cpYLKPufegZUWublDnIiDBdXdOkCYqKCyz6mnXJf8rhQXaBcBcZOD56BUUFmkgso+pwpKM/jEZQ6OoHSIL4tUUNnn1HvCHkW6MF7cVeJeQWkElUUqqOxz6ilXlFdoHUGkRQ5eqKvj4Vnk3q8/9zlVUPmeUxMKRb4kz7NOsAP3EuUwFVT2OVZQ7m26I7JFvuog1FRQ2efUOah8Tw8BcZeDIyjJIr06ZZ9Tx9SaUknrCCItSqasE4glFVT2bbQOsK2mVJN1BJEWqaDCTQWVfRusA2xLBSUua3KvoNxLlMNUUNm33jrAthpTDdYRRFrkYEHVWwcIExVU9jlVUHWba60jiLRo/WbrBDvQEyaLVFDZ59QhvlWbV1pHEGlR7SbrBDuosw4QJiqo7HNrBNVQx+ake29TRQBq3TugpoLKIhVU9jlVUACr6zWKEjetcW8EpUN8WaSCyj7nCmpV/SrrCCI7pRFUuKmgsiweTWzAsfNQKzWCEkc5eA5KI6gsUkHZWGYdYFs6xCeucnAE9Yl1gDBRQdlw6kG+sn6FdQSRnXJwBLXEOkCYqKBsOFVQSzfoOSfuWbMJ6t1b6ERPlixSQdlw6kH+bt186wgiO/jIzbM9S60DhIkKysaH1gG29emmZdRqRQlxzIfuPSQ3A59ZhwgTFZSNxdYBtrdg7TvWEUS+5MM11gl2sLSqQovFZpMKysZi6wDbW1CnghK3ODiCWmwdIGxUUDY+AJzaKVAFJS5Zswnq3JtiPs86QNiooAw0X6z7vnWObWmihLhk8foFa9fkPR5vYNmsFElXroN42zpA2BRYBwixGmB/6xBbbJko0aOoh3UUEd5YEy9ZXvg/kS1/L0oO+bBb8til3ZJHJTul9uvvUTTMw/OyHGtuln9e6Kmg7LwJnG0dYltz1rzBUX0i1jFEePPzmi/9fXPe4sGr8/4weDV/ACAvVVLbNXn4eyXJ49YXJ8d2z6fn/h5e1w6OpRFUlqmg7LxpHWB7M1a8pIISc2sb6nhrTc0u/03SW9tjbf7TB6/Nfzr9hVReU3HqwPndksev6Jo8tKAoNWiwR8HADMb6pKoC9+YV5jgVlB3nCmrmipdoSjWR7+VbR5EQS6ycSVOqjUtIeMn8jd6c8o15c8q3nLAqSPVb1q3pmMUlyWMaOqdG9vbosr+H197XPI2eDKigjMSjiaWRmL8KKLXOssWahs+ZXzuXA3uOsY4iITZjxYsZuZ1G79P+awr+0X8N/wDAS3Xe0DU5cW635HGfd0ke3LWA3sM98nq28ubeyEgoaRMVlK03geOsQ2xrxooXVVBipiHZQGLVjA657ZS3qcu6/BfHrstvLsAUqU6p4Qu7JY9b1i15BEWpfQfmUbRvC9/+SoeEkl1SQdl6HQcL6uLhl1nHkJCa8/kbrG/M0p6eHl69996w+rz3hq2iCoD8VM9VXZNHLixJHrexOHlgzzy6j/DwOqOCMqGCsvUCcLV1iG0tXr+IjzcsYWCXva2jSAi9nKHDe+3V5K0prcuvLq3Lr05/IVXQ0CU1/pknT7rjU9NgIaULdW09Dzi3oYD1i4SEV6bOP2WM11i4IW+WU9vjhIkKylA8mqgDXrXOsb1/LXvKOoKE0JzPa/h0k1ObTW/xgnWAsFJB2ZtuHWB7769dwNw1b1nHkJB5bOn/WkdoiWPDuvBQQdlzrqDA6RcLyUGr61fxwnInnwqfxKOJBdYhwkoFZe9lwLl1m+OfPsOazZ9bx5CQ+OfH02hMNVrH2Jlq6wBhpoIyFo8mNuLgFNaGVANPfvyEdQwJgaZkI098/Kh1jJZMsw4QZiooNzxjHWBnpi19pO1Lzoi00cyVM/hs03LrGDuzAXjWOkSYqaDc4OTbx083LWPWSucGd5JjHl/6sHWElsTi0cQm6xBhpoJyQDyamIuju3X+70f/sI4gOezDdYuYvSphHaMlOrxnTAXljoesA+zMq6sTvPm51smUjnHfwnusI7QkiSZImFNBueNB6wAtuee9O60jSA6aXzuX5z9zcmo5QCIeTXxmHSLsVFCOiEcT83B0S+m5tW/x0me6mF4yy/E3Po9ZBxAVlGucPMwH8Pv3f6cZfZIxs1cleOPz16xjtKQJ+It1CFFBucbZw3yL1y8i1rxG37vvvsu4ceO2fnTv3p2pU6fy0EMPccABB5CXl8err+56icGmpiYOOuggKioqtn7t3HPPZcyYMUyZMmXr12688UYee0xvZnNJMpV0ffT0dDya0AKxDlBBOSQeTcwH5ljnaMl9C+9hc1M9I0aMoKamhpqaGl577TW6dOnCGWecwYEHHsgjjzzC0Ucfvdvbuv322ykvL9/69zlz5lBcXMycOXOYPXs2tbW1LFu2jEQiwemnn96Rd0uyLL78Wd5b+651jF25zzqApKmg3HOvdYCWfLZpOY9ud83Ks88+y7Bhwxg8eDDl5eWMGDFit7ezdOlS/vnPf3LRRRdt/VphYSEbN24kmUzS0NBAfn4+1113Hddff33G74fYaUg28D/v320dY1dWounlzlBBuedPwDrrEC25f+G9fLrxi6MfDzzwAOecc06bbuPyyy/nlltuIS/vi4dfeXk5ZWVljB8/nlNPPZX333+fZDLJ+PHjM5Zd7P150R/4eONS6xi78td4NNFgHULSVFCOad4j6q/WOVqysWkjt8y7iWQqyebNm5k2bRpnnXVWq7+/urqaPn36MGHChB3+29SpU6mpqeGqq67i2muv5YYbbuCmm27i7LPP5t57nR1YSistqHuHvy663zrG7ujwnkNUUG76nXWAXXl99as8sfRRnnrqKcaPH0/fvn1b/b0vv/wy06ZNY8iQIXz9619n+vTpnHfeeV/6N48//jgTJkxg3bp1LFy4kAcffJCHH36YDRs2ZPquSJY0JBu4ee4Nrs8EfS0eTTh7DjiMVFAOan6SvGSdY1fufu8O7vvLfW0+vPeLX/yCpUuXsnjxYh544AGOO+44/vKXL2b0NjQ0MHXqVCZPnszGjRvxPA9Iz/rbvHlzRu+DZM+fPriPD9a9bx1jd5xd1iKsVFDucnoUtW7dOv4Ve5rTz/hiht2jjz7KoEGDmDlzJl/5ylc46aSTAPjkk0845ZRTWnW7d955JxdccAFdunRhzJgxbNiwgdGjRzNhwgR69uzZIfdFOtaCunf42+I/WsfYneWkz/+KQ7xUKmWdQXYiEvOLgI+A1h8/M3D5yGs4fe9/t44hjmpINlCZ+GYQRk9T4tHEL6xDyJdpBOWoeDSxGXB6Pi5A1Xv/zccblljHEEfdv/DeIJRTHY4fsQgrFZTbbif95HHWxqaN/KjmGtY3rreOIo55Yflz/NX9Q3sAVfFootY6hOxIBeWweDTxOfBb6xy7s3j9Im56+yckU0nrKOKI99e+x8/f/ql1jNbYDEy1DiE7p4Jy3204PooCmLHiRe5bWGUdQxywZvPn/LjmGjYlA7EZ7Z+17p67VFCOi0cTq4E7rHO0xl8W3c+zn/7LOoYYakg28JM5P+TTTcuso7RGCviVdQhpmQoqGH5DAEZRAL+ceyPv1r1jHUOM/PadW4O0A/Nf49GE06vWhp0KKgCCNIranKznxzXXsLp+lXUUybLHljzMEx8/ah2jtTYBP7IOIbumggqO3wBrrUO0xor6z5j8xuWsbQjEoE8y4IXlz3HHu7+xjtEWt8ejiY+sQ8iuqaAConkUdZN1jtZ6f+0CJr9+uaafh8DMFS/xs7d+7Po6e9taCfzcOoTsngoqWG4DFliHaK35dXP5wRtXsLFRi7zmqtmrElz35g9oTDVaR2mL65t3DRDHqaACpHl1icutc7TFW2ve5PtvXMEGjaRyzqyVr/CjmmtoSAVq+6QFgK6HCAgVVMDEo4mngGrrHG0xZ00NV7/+X6xrCMQpNGmFGSte5Ec1V7M5WW8dpa1+oA0Jg0MFFUyXA4F6ZZhX+zZXvHYZtZvXWEeRPfT88ulc++b3gzZyApgejyYCM81QVFCBFI8mFgK3Wudoq/fWvsulsy/iw3WLrKNIOz2w+C/8dM6UIE2I2GIjcLF1CGkbFVRw/RxYah2irT7esITvzrqQmSuc3o9RtlPftImb3v4pd793BykCuUXPdc1v7CRAVFABFY8m1gOXWSzOIb0AAAk6SURBVOdojw1N65lSczV/W6T94YJgxabP+N6r3yW27CnrKO01m/QMWAkYbVgYcJGY/wfgm9Y52uuEfidxzagpdMrvbB1FdmJe7dtc++b3WVW/0jpKezUAE+LRxFvWQaTtNIIKvu8BH1qHaK9nPn2a/3q1khWbPrOOItv51ydPcfmr3w1yOQHcrHIKLo2gckAk5h8DPAd41lnaa6+iXlxV/kOO7HO0dZTQ29C4nrvfu4NpSwM/4W0ecFDz9YMSQCqoHBGJ+b8BrrDOsaei/f6N/zfySroX9rCOEkqvrZrFLfNuYvmmT62j7KkG4Mh4NDHLOoi0nwoqR0RifmfgNWCUdZY91auoF1dqNJVV6xvXU5Ubo6YtropHE4FavVZ2pILKIZGYPx54BSi0zpIJGk1lRw6NmraYFo8mTrMOIXtOBZVjIjH/KuDX1jkypVdRLy4fOZmj+x5rHSXnrGtYS9V7dwZpD6fWWAyMj0cTn1sHkT2ngspBkZj/EPDv1jkyaXTPcVw8/FJG9xxrHSXw6ps28ciSh/jboj+xtjGnFvXWeacco4LKQZGYXwLMAkZaZ8m0w8uO4qJhlzC0ZD/rKIHTlGzkqU+quf+D37OyfoV1nI5wRTyamGodQjJHBZWjIjG/HEgAJdZZMs3D48T+J/OtYd+hX/EA6ziB8Pzy6fz+/btYsiFnN5F9NB5NnGkdQjJLBZXDIjH/VOAxcvSC7EKvkK8OOpOzBp9Dv+L+1nGc05RqYtbKV7j/g3t5t26+dZyO9DbpQ3u11kEks1RQOS4S86cQoK3i2yOPPA4tO4LTB03i4FKfPC8n+7jVajev4clPnmDa0kdYtvET6zgdbRlwaDyayNmhYZipoEIgEvP/DnzdOkc2DCwexFcHncm/DaigR1G4pqfPq32bx5Y8THz5s2xOhmLxhPXA0fFo4nXrINIxVFCO8zzvPqAC+CyVSh3YntuIxPxOwD+B4zOZzWVFeUUc1zfKVwadxgE9RufsqGptQx0vfPYcjy95hAVr37GOk01NwGnxaOKf1kGk46igHOd53tHAOuBP7S0ogEjM70Z6vb6DM5UtKPYq6sVhvY/g8LKjmVA6keL8YutIe+TjDUuZueIlXl7xAnPW1ARx88BMuDQeTdxlHUI6lgoqADzPGwJU70lBAURifm/gJWBEBmIFUlFeJyb0msjhZUdxWNkR9O5UZh1pt5pSTcyvncuMFS8xY8ULLF4f+h2Jb41HE1dbh5COp4IKgEwVFEAk5u8NzAAG7elt5YKh3fZjZPdy9u8+kv27j2RYt/3M96ZaWb+CBXXvsKDuHd6te4d5tW9T27DGNJNDHgD+Ix5N6IUrBFRQAZDJgoKt10i9CJRm4vZySb6Xz5CuQ79UWL07lVHaqTTjxVW7uZZV9StYtumTLxXS6s2rMvpzcsgjwNfi0USjdRDJDhVUAGS6oAAiMf8Q4FmgW6ZuM9d1Kyihd6felHbqTa9OvendqTe9ikopyisi38unIK+AfC+fZCpFU6qJplQTjalG1jeuY1X9ClbVr2Jl/QpW169iVf1KGlIN1ncpSJ4AJsWjCf1PCxEVVAB0REEBRGL+EaRn94VrPrYEzZPAGdp4MHxyc+5tDvE87+/ATGCE53lLPc+7MFO3HY8mXgaOBXJyYTbJCY+jcgotjaCESMwfCTwDDLTOIrKNh0lPiNBhvZDSCEqIRxPvAEcCH1hnEWn2Z+AclVO4aQQlW0Vi/gAgRg5sGy+BdkM8mrjOOoTYU0HJl0Rifinwf4RwxQkx1wBcHI8m7rcOIm7QIT75kng0sQo4GviHdRYJlVrgZJWTbEsjKGlR81YdNwKedRbJaR8Bp8SjibnWQcQtKijZpUjM/yrwF3JwZ15xwutARTyaWGYdRNyjQ3yyS/FoYhpwGLDQOovknD8AR6mcpCUaQUmrRGJ+L+Ah4DjrLBJ4G4DvxqOJP1kHEbdpBCWtEo8mVgMnAteT3ixOpD3mAhNVTtIaGkFJm0Vi/uGkz0vta51FAuV+4LJ4NLHBOogEgwpK2iUS87sD/w18wzqLOG8D6WK63zqIBIsKSvZIJOZ/Dbgb6GmdRZz0InBRPJpYYB1EgkfnoGSPxKOJfwBjgOets4hT1gKXAseonKS9NIKSjIjEfA+4ELgZ7dQbdk8Cl8SjiSXWQSTYVFCSUc1r+f0S+DZagSJsVgLfi0cTf7MOIrlBBSUdIhLzDwN+B4yzziIdLkV6VueV8WhipXUYyR0qKOkwkZifD/wn8DOgu3Ec6Rhx4Op4NPGadRDJPSoo6XCRmN8f+Anpw36FxnEkM+YD349HE09YB5HcpYKSrInE/KGki+pcIN84jrTPctK/w9/HowmtKCIdSgUlWReJ+SNJL5l0FppIERR1wO3ALfFoYp11GAkHFZSYicT8scANwKnWWaRFy4CpwN3xaKLOOoyEiwpKzEVi/gTge8DXgCLjOJK2APgV8Od4NFFvHUbCSQUlzojE/L7AJc0f/YzjhNUs0texPRaPJpLWYSTcVFDinEjMLwLOBv4LmGgcJwzWAw+TnvjwknUYkS1UUOK0SMw/FKgEJqFt5zNtBuldbf8RjybWWocR2Z4KSgIhEvO7AKeR3t7jRDRNvb0+Bf4E3BePJt61DiOyKyooCZxIzO9NekR1NnAMKqvdWQo8ATwOPBuPJhqN84i0igpKAi0S8/uQnqZ+InA8Wkl9i9eBacC0eDTxhnUYkfZQQUnOiMT8PGA86bI6ETiM8ExbXwO8RHqriyfi0cRS4zwie0wFJTkrEvO7AhHgWGAC6fLKlUVrPwQSpHesfRF4S9PCJdeooCQ0mjdV3I8vymrLZ5e3q28EFgLvAHNJX6eUiEcTn5qmEskCFZSEWnNpDQGGNX/ed7vP/ej49QI3AyuAj0gX0bvNn98BFmpSg4SVCkpkFyIxvzMwGCgjfXhwy0eP7f5cDDSRHvE0bffnxuaP1cBnpMto6+d4NFGbvXskEhwqKBERcVKedQAREZGdUUGJiIiTVFAiIuIkFZSIiDhJBSUiIk5SQYmIiJNUUCIi4iQVlIiIOEkFJSIiTlJBiYiIk1RQIiLiJBWUiIg4SQUlIiJOUkGJiIiTVFAiIuIkFZSIiDhJBSUiIk5SQYmIiJNUUCIi4iQVlIiIOOn/A3rh7dGKc98/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "دانشجویان\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "راضی\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "غذا\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "اساتید\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def BAR_PLOT(X,Y):\n",
        "  plt.figure(figsize=(40, 10))\n",
        "  plt.title(\"frequent word in document \")\n",
        "  plt.xlabel(\"Category\")\n",
        "  plt.ylabel(\"Number of word in document \")\n",
        "  plt.xticks(rotation=90)\n",
        "  sns.barplot(X, Y)\n",
        "  plt.show()\n"
      ],
      "metadata": {
        "id": "lsz6yc44XIiA"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BAR_PLOT(vocab[570:],vector[570:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "RE3KOeaOaMJb",
        "outputId": "fc9ce0ed-e192-40d0-ff31-3864b4ec103a"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BAR_PLOT(vocab_pos[210:],vector_pos[210:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "H7uauZrMbnta",
        "outputId": "76445daf-2639-4479-db61-08f838463853"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BAR_PLOT(vocab_neg[210:],vector_neg[210:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "G9cT4bUad22d",
        "outputId": "1080ea2b-e610-412a-9c2f-530751ca7d28"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Word** **Clouds**"
      ],
      "metadata": {
        "id": "WX0khO0sAiiD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install persian_wordcloud\n",
        "!pip install wordcloud-fa\n",
        "!sudo apt-get install python3-dev"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEf7ayc81_qV",
        "outputId": "63e71f54-6e6e-4a66-de73-9074a59a69ef"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting persian_wordcloud\n",
            "  Downloading persian_wordcloud-1.3.1.tar.gz (65 kB)\n",
            "\u001b[K     |████████████████████████████████| 65 kB 3.5 MB/s \n",
            "\u001b[?25hCollecting arabic_reshaper\n",
            "  Downloading arabic_reshaper-2.1.3-py3-none-any.whl (20 kB)\n",
            "Collecting python-bidi\n",
            "  Downloading python_bidi-0.4.2-py2.py3-none-any.whl (30 kB)\n",
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.7/dist-packages (from persian_wordcloud) (1.5.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from arabic_reshaper->persian_wordcloud) (57.4.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from arabic_reshaper->persian_wordcloud) (0.16.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from python-bidi->persian_wordcloud) (1.15.0)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud->persian_wordcloud) (7.1.2)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from wordcloud->persian_wordcloud) (1.21.6)\n",
            "Building wheels for collected packages: persian-wordcloud\n",
            "  Building wheel for persian-wordcloud (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for persian-wordcloud: filename=persian_wordcloud-1.3.1-py3-none-any.whl size=65942 sha256=5cd6ce9ca878abe3f2e9aafdd6e96a6c43a6b37bad10c0a3791bf56b42aab430\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/84/30/3f71371632c768bc2a5679e72db807c9b60bc890661f6fbde5\n",
            "Successfully built persian-wordcloud\n",
            "Installing collected packages: python-bidi, arabic-reshaper, persian-wordcloud\n",
            "Successfully installed arabic-reshaper-2.1.3 persian-wordcloud-1.3.1 python-bidi-0.4.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wordcloud-fa\n",
            "  Downloading wordcloud_fa-0.1.9-py2.py3-none-any.whl (71 kB)\n",
            "\u001b[K     |████████████████████████████████| 71 kB 4.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.1.2 in /usr/local/lib/python3.7/dist-packages (from wordcloud-fa) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.7/dist-packages (from wordcloud-fa) (1.21.6)\n",
            "Collecting wordcloud==1.8.1\n",
            "  Downloading wordcloud-1.8.1-cp37-cp37m-manylinux1_x86_64.whl (366 kB)\n",
            "\u001b[K     |████████████████████████████████| 366 kB 16.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: hazm==0.7.0 in /usr/local/lib/python3.7/dist-packages (from wordcloud-fa) (0.7.0)\n",
            "Requirement already satisfied: pillow>=7.0.0 in /usr/local/lib/python3.7/dist-packages (from wordcloud-fa) (7.1.2)\n",
            "Requirement already satisfied: arabic-reshaper>=2.1.3 in /usr/local/lib/python3.7/dist-packages (from wordcloud-fa) (2.1.3)\n",
            "Requirement already satisfied: python-bidi==0.4.2 in /usr/local/lib/python3.7/dist-packages (from wordcloud-fa) (0.4.2)\n",
            "Requirement already satisfied: libwapiti>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from hazm==0.7.0->wordcloud-fa) (0.2.1)\n",
            "Requirement already satisfied: nltk==3.3 in /usr/local/lib/python3.7/dist-packages (from hazm==0.7.0->wordcloud-fa) (3.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk==3.3->hazm==0.7.0->wordcloud-fa) (1.15.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from arabic-reshaper>=2.1.3->wordcloud-fa) (0.16.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from arabic-reshaper>=2.1.3->wordcloud-fa) (57.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.2->wordcloud-fa) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.2->wordcloud-fa) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.2->wordcloud-fa) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.2->wordcloud-fa) (1.4.3)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.1.2->wordcloud-fa) (4.1.1)\n",
            "Installing collected packages: wordcloud, wordcloud-fa\n",
            "  Attempting uninstall: wordcloud\n",
            "    Found existing installation: wordcloud 1.5.0\n",
            "    Uninstalling wordcloud-1.5.0:\n",
            "      Successfully uninstalled wordcloud-1.5.0\n",
            "Successfully installed wordcloud-1.8.1 wordcloud-fa-0.1.9\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "python3-dev is already the newest version (3.6.7-1~18.04).\n",
            "python3-dev set to manually installed.\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud_fa import WordCloudFa\n",
        "\n",
        "wodcloud = WordCloudFa(no_reshape=False, persian_normalize=True, include_numbers=False, collocations=False, width=800, height=400)\n",
        "\n",
        "with open('/content/sample_data/c__data_df.txt', 'r') as file:\n",
        "\n",
        "     text = file.read()\n",
        "\n",
        "\n",
        "\n",
        "wc = wodcloud.generate(text)\n",
        "image = wc.to_image()\n",
        "image.show()\n",
        "image.save('dfdata.png')"
      ],
      "metadata": {
        "id": "_yoGKr_r6Vj_"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud_fa import WordCloudFa\n",
        "\n",
        "wodcloud = WordCloudFa(no_reshape=False, persian_normalize=True, include_numbers=False, collocations=False, width=800, height=400)\n",
        "\n",
        "with open('/content/sample_data/c__data_posetive (2).txt', 'r') as file:\n",
        "\n",
        "     text = file.read()\n",
        "\n",
        "\n",
        "\n",
        "wc = wodcloud.generate(text)\n",
        "image = wc.to_image()\n",
        "image.show()\n",
        "image.save('posdata.png')"
      ],
      "metadata": {
        "id": "0ZQikpJu8mLS"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud_fa import WordCloudFa\n",
        "\n",
        "wodcloud = WordCloudFa(no_reshape=False, persian_normalize=True, include_numbers=False, collocations=False, width=800, height=400)\n",
        "#text = \"سلام\"\n",
        "with open('/content/sample_data/c__data_negative (1).txt', 'r') as file:\n",
        "\n",
        "     text = file.read()\n",
        "\n",
        "wc = wodcloud.generate(text)\n",
        "image = wc.to_image()\n",
        "image.show()\n",
        "image.save('negdata.png')"
      ],
      "metadata": {
        "id": "letBIOva8mxL"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TFIDF**"
      ],
      "metadata": {
        "id": "5kpITHl88LyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Define a function to calculate Term Frequency\n",
        "#Term Frequency\n",
        "def termfreq(document, word):\n",
        "    \n",
        "    N = len(document.split())\n",
        "    #print(N)\n",
        "    occurance = len([token for token in document.split() if token == word])\n",
        "    return occurance/N"
      ],
      "metadata": {
        "id": "5-h2LhKf8Ope"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Inverse Document Frequency\n",
        "#Define a function calculate Inverse Document Frequency         neg_Doc_v.word2count\n",
        "def inverse_doc_freq(word,doc):\n",
        "    try:\n",
        "        word_occurance = doc.word2count[word] + 1\n",
        "    except:\n",
        "        word_occurance = 1\n",
        "    return np.log(doc.num_sentences/word_occurance)"
      ],
      "metadata": {
        "id": "eRYYd78b8XQR"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Combining the TF-IDF functions\n",
        "\n",
        "def tf_idf(sentence,doc):\n",
        "    tf_idf_vec = np.zeros((doc.num_words,))\n",
        "    for word in sentence.split():\n",
        "        tf = termfreq(sentence,word)\n",
        "        idf = inverse_doc_freq(word,doc)\n",
        "         \n",
        "        value = tf*idf\n",
        "        tf_idf_vec[doc.word2index[word]] = value \n",
        "    return tf_idf_vec"
      ],
      "metadata": {
        "id": "Ck6tdI_n8cSY"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Apply the TF-IDF Model to our text\n",
        "#TF-IDF Encoded text corpus     df.index         df.loc[x, \"Comment\"]\n",
        "vectors = []\n",
        "for x in df.index :\n",
        "    vec = tf_idf(df.loc[x, \"Comment\"],voc_Doc)\n",
        "    vectors.append(vec)\n",
        " \n",
        "print(vectors[40])\n",
        "print(len(vectors))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6jmJ5XX8g2M",
        "outputId": "d253c03a-c78b-4d4e-b452-720bc3372b9e"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " 0.         1.01403084 1.18731763 1.08595136 1.08595136 0.\n",
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            " 0.        ]\n",
            "231\n"
          ]
        }
      ]
    }
  ]
}