MSc_RNN & LSTM.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["### Importing Required Libraries"],"metadata":{"id":"P3Fy1i3_TYix"}},{"cell_type":"code","source":["!pip install keras\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6gqL9s3IufOA","outputId":"3f14e73f-d01a-4382-bfda-4b873858a07e","executionInfo":{"status":"ok","timestamp":1668128150994,"user_tz":0,"elapsed":14288,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (2.9.0)\n"]}]},{"cell_type":"code","source":["!pip install pad_sequences"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OiXjGoKturt7","outputId":"e6832fa6-cd83-4574-d448-b33c2e3f3384","executionInfo":{"status":"ok","timestamp":1668128159268,"user_tz":0,"elapsed":8290,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"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 pad_sequences\n","  Downloading pad-sequences-0.6.1.tar.gz (9.5 kB)\n","Building wheels for collected packages: pad-sequences\n","  Building wheel for pad-sequences (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pad-sequences: filename=pad_sequences-0.6.1-py3-none-any.whl size=10216 sha256=9b9a0ea8cca773a4aa551b98dddda78648c4d296c51a21a4aa0f694e374b5e7e\n","  Stored in directory: /root/.cache/pip/wheels/3f/80/05/48e28be7b6bde8b3378f747f50bc32a87cb281c2e89ae74500\n","Successfully built pad-sequences\n","Installing collected packages: pad-sequences\n","Successfully installed pad-sequences-0.6.1\n"]}]},{"cell_type":"code","source":["!pip install tensorflow"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uL4slNsvFIho","outputId":"f2f384f4-09f6-4167-c874-f481b080cd5b","executionInfo":{"status":"ok","timestamp":1668128164618,"user_tz":0,"elapsed":5385,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (2.9.2)\n","Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.2.0)\n","Requirement already satisfied: flatbuffers<2,>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.12)\n","Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.2)\n","Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.14.1)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow) (57.4.0)\n","Requirement already satisfied: tensorboard<2.10,>=2.9 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.9.1)\n","Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.27.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow) (21.3)\n","Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.4.0)\n","Requirement already satisfied: 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(3.10.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.4.8)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2022.9.24)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (1.24.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (3.2.2)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow) (3.0.9)\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wVpKrlVLEnqg"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import re\n","import matplotlib.pyplot as plt\n","import string\n","import nltk\n","from nltk.corpus import stopwords\n","from nltk.corpus import wordnet\n","from nltk.stem import WordNetLemmatizer\n","from nltk.tokenize.treebank import TreebankWordDetokenizer\n","from collections import Counter\n","from wordcloud import WordCloud\n","from gensim.utils import simple_preprocess\n","import gensim\n","from sklearn.model_selection import train_test_split\n","import spacy\n","import pickle\n","import warnings\n","warnings.filterwarnings('ignore')\n","import seaborn as sns\n","from sklearn.metrics import confusion_matrix\n","import matplotlib.pyplot as plt \n","import tensorflow as tf\n","import keras\n","from keras.models import Sequential\n","from keras import layers\n","from keras.optimizers import RMSprop,Adam\n","from keras.preprocessing.text import Tokenizer\n","from keras_preprocessing.sequence import pad_sequences\n","from keras import regularizers\n","from keras import backend as K\n","from keras.callbacks import ModelCheckpoint\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import classification_report,confusion_matrix"]},{"cell_type":"markdown","source":["### Authentication to Google Drive Files"],"metadata":{"id":"lYHeR4DTTVq1"}},{"cell_type":"code","source":["# Code to read csv file into Colaboratory:\n","!pip install -U -q PyDrive\n","from pydrive.auth import GoogleAuth\n","from pydrive.drive import GoogleDrive\n","from google.colab import auth\n","from oauth2client.client import GoogleCredentials\n","# Authenticate and create the PyDrive client.\n","auth.authenticate_user()\n","gauth = GoogleAuth()\n","gauth.credentials = GoogleCredentials.get_application_default()\n","drive = GoogleDrive(gauth)"],"metadata":{"id":"eXCQTfXFTgSv"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Access to file"],"metadata":{"id":"kavtTHj6T0Kf"}},{"cell_type":"code","source":["# link = 'https://drive.google.com/file/d/17VnKTmYjjqYQ1WtBDIhSyEhIV6Ennu0N/view?usp=share_link\n","id = '17VnKTmYjjqYQ1WtBDIhSyEhIV6Ennu0N'"],"metadata":{"id":"dZx2fSgaTk6Y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["downloaded = drive.CreateFile({'id':id}) \n","downloaded.GetContentFile('FlairAnalysis.csv',)  "],"metadata":{"id":"V7qsoUxdToMq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Load dataset"],"metadata":{"id":"ugRGCifEUJX1"}},{"cell_type":"code","source":["data = pd.read_csv('FlairAnalysis.csv',index_col=[0])\n","data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"wE2SqvSCPBZ6","outputId":"a6b042cd-6fdd-46cd-9d08-9e4372d09b87","executionInfo":{"status":"ok","timestamp":1668128210830,"user_tz":0,"elapsed":3580,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                     text  \\\n","0       RT @StrikeMapUK: The Cost of Living Crisis is ...   \n","1       RT @0Calamity: This is not a #CostOfLivingCris...   \n","2       RT @geoffh33: Every non-Tory MP should have st...   \n","3       RT @hewitson10: Ian Blackford owned the show a...   \n","4       RT @snowy2007: Serious question for Scottish P...   \n","...                                                   ...   \n","541723  RT @SB_Sarah10: Do you honestly think #LizTrus...   \n","541724  RT @michaelbartrum: Every hour, 10 people die ...   \n","541725  Jesus! £170 billion excess profits over 2 yrs!...   \n","541726  RT @maxwellwoodger: Attended the Final @Conser...   \n","541727  #BorisJohnson plans to visit sites of his grea...   \n","\n","                                           Cleaned_tweets  probability  \\\n","0       cost living crisis political choice yet tory t...     0.924938   \n","1       costoflivingcrisis stateimposedpoverty do quit...     0.865847   \n","2       every non tory mp stand today call borisjohnso...     0.987879   \n","3       ian blackford own show always tory care costof...     0.544504   \n","4       serious question scottish power say energy gre...     0.600847   \n","...                                                   ...          ...   \n","541723    honestly think liztruss plan cost living crisis     0.993839   \n","541724  every hour people die poverty uk rise costofli...     0.996808   \n","541725  jesus billion excess profit yrs time take stre...    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£170 billion excess profits over 2 yrs!...   \n","541726  RT @maxwellwoodger: Attended the Final @Conser...   \n","541727  #BorisJohnson plans to visit sites of his grea...   \n","\n","                                           Cleaned_tweets  probability  \\\n","0       cost living crisis political choice yet tory t...     0.924938   \n","1       costoflivingcrisis stateimposedpoverty do quit...     0.865847   \n","2       every non tory mp stand today call borisjohnso...     0.987879   \n","3       ian blackford own show always tory care costof...     0.544504   \n","4       serious question scottish power say energy gre...     0.600847   \n","...                                                   ...          ...   \n","541723    honestly think liztruss plan cost living crisis     0.993839   \n","541724  every hour people die poverty uk rise costofli...     0.996808   \n","541725  jesus billion excess profit yrs time take stre...     0.689607   \n","541726  attend final leadership debate uk husting fell...     0.690461   \n","541727     borisjohnson plan visit site great achievement     0.997348   \n","\n","       sentiment  \n","0       NEGATIVE  \n","1       POSITIVE  \n","2       NEGATIVE  \n","3       NEGATIVE  \n","4       POSITIVE  \n","...          ...  \n","541723  NEGATIVE  \n","541724  NEGATIVE  \n","541725  NEGATIVE  \n","541726  NEGATIVE  \n","541727  POSITIVE  \n","\n","[541512 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-874c6045-d0da-42ab-bcf2-81a48257cbe1\">\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>text</th>\n","      <th>Cleaned_tweets</th>\n","      <th>probability</th>\n","      <th>sentiment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>RT @StrikeMapUK: The Cost of Living Crisis is ...</td>\n","      <td>cost living crisis political choice yet tory t...</td>\n","      <td>0.924938</td>\n","      <td>NEGATIVE</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>RT @0Calamity: This is not a #CostOfLivingCris...</td>\n","      <td>costoflivingcrisis stateimposedpoverty do quit...</td>\n","      <td>0.865847</td>\n","      <td>POSITIVE</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>RT @geoffh33: Every non-Tory MP should have st...</td>\n","      <td>every non tory mp stand today call borisjohnso...</td>\n","      <td>0.987879</td>\n","      <td>NEGATIVE</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>RT @hewitson10: Ian Blackford owned the show a...</td>\n","      <td>ian blackford own show always tory care costof...</td>\n","      <td>0.544504</td>\n","      <td>NEGATIVE</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>RT @snowy2007: Serious question for Scottish P...</td>\n","      <td>serious question scottish power say energy gre...</td>\n","      <td>0.600847</td>\n","      <td>POSITIVE</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>541723</th>\n","      <td>RT @SB_Sarah10: Do you honestly think #LizTrus...</td>\n","      <td>honestly think liztruss plan cost living crisis</td>\n","      <td>0.993839</td>\n","      <td>NEGATIVE</td>\n","    </tr>\n","    <tr>\n","      <th>541724</th>\n","      <td>RT @michaelbartrum: Every hour, 10 people die ...</td>\n","      <td>every hour people die poverty uk rise costofli...</td>\n","      <td>0.996808</td>\n","      <td>NEGATIVE</td>\n","    </tr>\n","    <tr>\n","      <th>541725</th>\n","      <td>Jesus! £170 billion excess profits over 2 yrs!...</td>\n","      <td>jesus billion excess profit yrs time take stre...</td>\n","      <td>0.689607</td>\n","      <td>NEGATIVE</td>\n","    </tr>\n","    <tr>\n","      <th>541726</th>\n","      <td>RT @maxwellwoodger: Attended the Final @Conser...</td>\n","      <td>attend final leadership debate uk husting fell...</td>\n","      <td>0.690461</td>\n","      <td>NEGATIVE</td>\n","    </tr>\n","    <tr>\n","      <th>541727</th>\n","      <td>#BorisJohnson plans to visit sites of his grea...</td>\n","      <td>borisjohnson plan visit site great achievement</td>\n","      <td>0.997348</td>\n","      <td>POSITIVE</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>541512 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-874c6045-d0da-42ab-bcf2-81a48257cbe1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </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-874c6045-d0da-42ab-bcf2-81a48257cbe1 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-874c6045-d0da-42ab-bcf2-81a48257cbe1');\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":10}]},{"cell_type":"code","source":["#Is there any other different value than neutral, negative and positive?\n","newdata['sentiment'].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j2wcAF4rsuFL","outputId":"844db2c4-1c92-4946-90af-70f0d759f038","executionInfo":{"status":"ok","timestamp":1668128211124,"user_tz":0,"elapsed":305,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['NEGATIVE', 'POSITIVE'], dtype=object)"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["temp = []\n","#Splitting pd.Series to list\n","data_to_list = data['Cleaned_tweets'].values.tolist()\n","for i in range(len(data_to_list)):\n","    temp.append(data_to_list[i])\n","list(temp[:5])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7Wpntb0Is6z3","outputId":"d6a6d869-ebe6-45ec-ecc8-2c13cf4c0ab6","executionInfo":{"status":"ok","timestamp":1668128211352,"user_tz":0,"elapsed":240,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['cost living crisis political choice yet tory try make ordinary people pay join ppls',\n"," 'costoflivingcrisis stateimposedpoverty do quite deliberately able please',\n"," 'every non tory mp stand today call borisjohnson liar hoyle would ask leave chamb',\n"," 'ian blackford own show always tory care costoflivingcrisis go piss',\n"," 'serious question scottish power say energy green justify rise']"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["def sent_to_words(sentences):\n","    for sentence in sentences:\n","        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n","        \n","\n","data_words = list(sent_to_words(temp))\n","\n","print(data_words[:10])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MO-A5hrws6sf","outputId":"ba255cbb-d101-437c-c9b7-24f8700df9ef","executionInfo":{"status":"ok","timestamp":1668128260118,"user_tz":0,"elapsed":47253,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[['cost', 'living', 'crisis', 'political', 'choice', 'yet', 'tory', 'try', 'make', 'ordinary', 'people', 'pay', 'join', 'ppls'], ['do', 'quite', 'deliberately', 'able', 'please'], ['every', 'non', 'tory', 'mp', 'stand', 'today', 'call', 'borisjohnson', 'liar', 'hoyle', 'would', 'ask', 'leave', 'chamb'], ['ian', 'blackford', 'own', 'show', 'always', 'tory', 'care', 'go', 'piss'], ['serious', 'question', 'scottish', 'power', 'say', 'energy', 'green', 'justify', 'rise'], ['brian', 'see', 'impact', 'every', 'time', 'volunteer', 'food', 'bank', 'nobody', 'get'], ['conservative', 'snp', 'ferryfiasco', 'nhs', 'ukraine', 'borisjohnson', 'partygate', 'oligarchs', 'covidisntover', 'nhs'], [], ['worth', 'repeat', 'thank', 'know', 'headline', 'inflation', 'worth', 'repeat', 'uk', 'low', 'state', 'pension', 'west', 'europe', 'worth', 'repeat', 'euro', 'nation', 'much', 'energy', 'price', 'crisis'], ['do', 'quite', 'deliberately', 'able', 'please']]\n"]}]},{"cell_type":"code","source":["def detokenize(text):\n","    return TreebankWordDetokenizer().detokenize(text)"],"metadata":{"id":"P1l-NpBxs6l1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["newdata = []\n","for i in range(len(data_words)):\n","    newdata.append(detokenize(data_words[i]))\n","print(newdata[:5])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vqiVerycst_C","outputId":"1f371fe9-9a21-484a-be5e-710f313f62f0","executionInfo":{"status":"ok","timestamp":1668128305423,"user_tz":0,"elapsed":45326,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['cost living crisis political choice yet tory try make ordinary people pay join ppls', 'do quite deliberately able please', 'every non tory mp stand today call borisjohnson liar hoyle would ask leave chamb', 'ian blackford own show always tory care go piss', 'serious question scottish power say energy green justify rise']\n"]}]},{"cell_type":"code","source":["newdata = np.array(newdata)\n","newdata"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qzw71Bn5st49","outputId":"376516cf-d52b-4346-9984-61b2456950fb","executionInfo":{"status":"ok","timestamp":1668128306154,"user_tz":0,"elapsed":740,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['cost living crisis political choice yet tory try make ordinary people pay join ppls',\n","       'do quite deliberately able please',\n","       'every non tory mp stand today call borisjohnson liar hoyle would ask leave chamb',\n","       ...,\n","       'jesus billion excess profit yrs time take street enoughlsenough energycrisis',\n","       'attend final leadership debate uk husting fellow tory londoner vs rishi',\n","       'borisjohnson plan visit site great achievement'], dtype='<U283')"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["Encoding: Convert sentiment Positve or Negative to binary 1 or 0"],"metadata":{"id":"Xp02SNwOUesc"}},{"cell_type":"code","source":["labels = np.array(data['sentiment'])\n","y = []\n","for i in range(len(labels)):\n","    if labels[i] == 'NEGATIVE':\n","        y.append(0)\n","    if labels[i] == 'POSITIVE':\n","        y.append(1)\n","y = np.array(y)\n","labels = tf.keras.utils.to_categorical(y, 3, dtype=\"float32\")\n","del y"],"metadata":{"id":"cWmFQ5p-tyL8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(labels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RNnXkOdDtyHj","outputId":"9af9808e-a9ba-46ea-9478-39060d1d8b7b","executionInfo":{"status":"ok","timestamp":1668128306451,"user_tz":0,"elapsed":12,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["541512"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["max_words = 5000\n","max_len = 200\n","\n","tokenizer = Tokenizer(num_words=max_words)\n","tokenizer.fit_on_texts(newdata)\n","sequences = tokenizer.texts_to_sequences(newdata)\n","tweets = pad_sequences(sequences, maxlen=max_len)\n","print(tweets)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LbyB8nR7tZ3a","outputId":"efdf3086-1c4a-4139-b16d-3d7f67efea66","executionInfo":{"status":"ok","timestamp":1668128328891,"user_tz":0,"elapsed":22443,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[   0    0    0 ...    3   16  153]\n"," [   0    0    0 ... 2029  365   55]\n"," [   0    0    0 ...   29  125  102]\n"," ...\n"," [   0    0    0 ...  201 1214  148]\n"," [   0    0    0 ... 3107 1839  189]\n"," [   0    0    0 ... 2399  136 1961]]\n"]}]},{"cell_type":"code","source":["print(labels)"],"metadata":{"id":"u2kf02UutZzf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668128328892,"user_tz":0,"elapsed":32,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}},"outputId":"aec3ced4-aad7-402d-876a-ca5db12920bb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[1. 0. 0.]\n"," [0. 1. 0.]\n"," [1. 0. 0.]\n"," ...\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [0. 1. 0.]]\n"]}]},{"cell_type":"code","source":["len(labels)"],"metadata":{"id":"uUo4dqcaoXxu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668128328893,"user_tz":0,"elapsed":30,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}},"outputId":"93c93326-ee8c-4c36-a64e-691387c1adc0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["541512"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","source":["Train and split the data using 30%"],"metadata":{"id":"5QjvM1ZmUk4u"}},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(tweets,labels, test_size= 0.3,stratify=labels,random_state=42)\n","print (len(X_train),len(X_test),len(y_train),len(y_test))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6gDk1PrxtZvm","outputId":"9a6cdf58-f946-471c-d841-db2d5b8e7c11","executionInfo":{"status":"ok","timestamp":1668128332515,"user_tz":0,"elapsed":3648,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["379058 162454 379058 162454\n"]}]},{"cell_type":"markdown","source":["###SimpleRNN model"],"metadata":{"id":"IjVPySQQU6sH"}},{"cell_type":"code","source":["model0 = Sequential()\n","model0.add(layers.Embedding(max_words, 15))\n","model0.add(layers.SimpleRNN(15))\n","model0.add(layers.Dense(3,activation='relu'))\n","\n","model0.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n","\n","# Implementing model checkpoins to save the best metric and do not lose it on training.\n","checkpoint0 = ModelCheckpoint(\"best_model0.hdf5\", monitor='val_accuracy', verbose=1,save_best_only=True, mode='auto', period=1,save_weights_only=False)\n","history = model0.fit(X_train, y_train, epochs=5,validation_data=(X_test, y_test), callbacks=[checkpoint0])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QmaDZW9e58D2","outputId":"cfde1ff9-6e25-45c4-e769-874323da89da","executionInfo":{"status":"ok","timestamp":1668130975028,"user_tz":0,"elapsed":2642522,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","11845/11846 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.7170\n","Epoch 1: val_accuracy improved from -inf to 0.69100, saving model to best_model0.hdf5\n","11846/11846 [==============================] - 536s 45ms/step - loss: nan - accuracy: 0.7170 - val_loss: nan - val_accuracy: 0.6910\n","Epoch 2/5\n","11845/11846 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.6910\n","Epoch 2: val_accuracy did not improve from 0.69100\n","11846/11846 [==============================] - 530s 45ms/step - loss: nan - accuracy: 0.6910 - val_loss: nan - val_accuracy: 0.6910\n","Epoch 3/5\n","11846/11846 [==============================] - ETA: 0s - loss: nan - accuracy: 0.6910\n","Epoch 3: val_accuracy did not improve from 0.69100\n","11846/11846 [==============================] - 525s 44ms/step - loss: nan - accuracy: 0.6910 - val_loss: nan - val_accuracy: 0.6910\n","Epoch 4/5\n","11845/11846 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.6910\n","Epoch 4: val_accuracy did not improve from 0.69100\n","11846/11846 [==============================] - 525s 44ms/step - loss: nan - accuracy: 0.6910 - val_loss: nan - val_accuracy: 0.6910\n","Epoch 5/5\n","11845/11846 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.6910\n","Epoch 5: val_accuracy did not improve from 0.69100\n","11846/11846 [==============================] - 525s 44ms/step - loss: nan - accuracy: 0.6910 - val_loss: nan - val_accuracy: 0.6910\n"]}]},{"cell_type":"markdown","source":["###Single LSTM layer model"],"metadata":{"id":"jNfkoJDbVEku"}},{"cell_type":"code","source":["model1 = Sequential()\n","model1.add(layers.Embedding(max_words, 20))\n","model1.add(layers.LSTM(15,dropout=0.5))\n","model1.add(layers.Dense(3,activation='softmax'))\n","\n","model1.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])\n","\n","#Implementing model checkpoins to save the best metric and do not lose it on training.\n","checkpoint1 = ModelCheckpoint(\"best_model1.hdf5\", monitor='val_accuracy', verbose=1, save_best_only=True, mode='auto', period=1,save_weights_only=False)\n","history = model1.fit(X_train, y_train, epochs=5,validation_data=(X_test, y_test),callbacks=[checkpoint1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Qnh98zN157_g","outputId":"680aa26f-0af5-4567-9ec2-f8a4d928ea68","executionInfo":{"status":"ok","timestamp":1668136003053,"user_tz":0,"elapsed":5028049,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8095\n","Epoch 1: val_accuracy improved from -inf to 0.83248, saving model to best_model1.hdf5\n","11846/11846 [==============================] - 1010s 85ms/step - loss: 0.4165 - accuracy: 0.8095 - val_loss: 0.3710 - val_accuracy: 0.8325\n","Epoch 2/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3778 - accuracy: 0.8297\n","Epoch 2: val_accuracy improved from 0.83248 to 0.84159, saving model to best_model1.hdf5\n","11846/11846 [==============================] - 1008s 85ms/step - loss: 0.3778 - accuracy: 0.8297 - val_loss: 0.3521 - val_accuracy: 0.8416\n","Epoch 3/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3657 - accuracy: 0.8364\n","Epoch 3: val_accuracy improved from 0.84159 to 0.84531, saving model to best_model1.hdf5\n","11846/11846 [==============================] - 1004s 85ms/step - loss: 0.3657 - accuracy: 0.8364 - val_loss: 0.3530 - val_accuracy: 0.8453\n","Epoch 4/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3571 - accuracy: 0.8414\n","Epoch 4: val_accuracy improved from 0.84531 to 0.84653, saving model to best_model1.hdf5\n","11846/11846 [==============================] - 1001s 84ms/step - loss: 0.3571 - accuracy: 0.8414 - val_loss: 0.3437 - val_accuracy: 0.8465\n","Epoch 5/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3536 - accuracy: 0.8429\n","Epoch 5: val_accuracy improved from 0.84653 to 0.84737, saving model to best_model1.hdf5\n","11846/11846 [==============================] - 1005s 85ms/step - loss: 0.3536 - accuracy: 0.8429 - val_loss: 0.3427 - val_accuracy: 0.8474\n"]}]},{"cell_type":"markdown","source":["####Bidirectional LTSM model"],"metadata":{"id":"6fYa4IsGVMg_"}},{"cell_type":"code","source":["model2 = Sequential()\n","model2.add(layers.Embedding(max_words, 40, input_length=max_len))\n","model2.add(layers.Bidirectional(layers.LSTM(20,dropout=0.6)))\n","model2.add(layers.Dense(3,activation='softmax'))\n","model2.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])\n","#Implementing model checkpoins to save the best metric and do not lose it on training.\n","checkpoint2 = ModelCheckpoint(\"best_model2.hdf5\", monitor='val_accuracy', verbose=1,save_best_only=True, mode='auto', period=1,save_weights_only=False)\n","history = model2.fit(X_train, y_train, epochs=5,validation_data=(X_test, y_test),callbacks=[checkpoint2])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TGifN7oZIZb-","outputId":"e22e8dad-3fef-40dc-a471-a9824d252562","executionInfo":{"status":"ok","timestamp":1668144370231,"user_tz":0,"elapsed":8367204,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.4185 - accuracy: 0.8077\n","Epoch 1: val_accuracy improved from -inf to 0.83545, saving model to best_model2.hdf5\n","11846/11846 [==============================] - 1679s 141ms/step - loss: 0.4185 - accuracy: 0.8077 - val_loss: 0.3697 - val_accuracy: 0.8354\n","Epoch 2/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3730 - accuracy: 0.8332\n","Epoch 2: val_accuracy improved from 0.83545 to 0.84573, saving model to best_model2.hdf5\n","11846/11846 [==============================] - 1682s 142ms/step - loss: 0.3730 - accuracy: 0.8332 - val_loss: 0.3522 - val_accuracy: 0.8457\n","Epoch 3/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3604 - accuracy: 0.8400\n","Epoch 3: val_accuracy improved from 0.84573 to 0.84866, saving model to best_model2.hdf5\n","11846/11846 [==============================] - 1653s 140ms/step - loss: 0.3604 - accuracy: 0.8400 - val_loss: 0.3469 - val_accuracy: 0.8487\n","Epoch 4/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3556 - accuracy: 0.8420\n","Epoch 4: val_accuracy improved from 0.84866 to 0.85083, saving model to best_model2.hdf5\n","11846/11846 [==============================] - 1644s 139ms/step - loss: 0.3556 - accuracy: 0.8420 - val_loss: 0.3372 - val_accuracy: 0.8508\n","Epoch 5/5\n","11846/11846 [==============================] - ETA: 0s - loss: 0.3519 - accuracy: 0.8450\n","Epoch 5: val_accuracy improved from 0.85083 to 0.85231, saving model to best_model2.hdf5\n","11846/11846 [==============================] - 1651s 139ms/step - loss: 0.3519 - accuracy: 0.8450 - val_loss: 0.3357 - val_accuracy: 0.8523\n"]}]},{"cell_type":"markdown","source":["### Evaluating Best Model Obtained"],"metadata":{"id":"0pF0jPAsq5Uf"}},{"cell_type":"code","source":["#Let's load the best model obtained during training\n","best_model = keras.models.load_model(\"best_model2.hdf5\")\n","\n","test_loss, test_acc = best_model.evaluate(X_test, y_test, verbose=2)\n","print('Model accuracy: ',test_acc)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rCs_2xFAIzj4","outputId":"dc217dc0-595b-442b-8248-0db1e0d01d44","executionInfo":{"status":"ok","timestamp":1668152390108,"user_tz":0,"elapsed":144233,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["5077/5077 - 122s - loss: 0.3357 - accuracy: 0.8523 - 122s/epoch - 24ms/step\n","Model accuracy:  0.852308988571167\n"]}]},{"cell_type":"code","source":["predictions = best_model.predict(X_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"emtSGA-nKeUW","outputId":"c16e99b7-4ae4-4665-e5b2-ddbc529ef5df","executionInfo":{"status":"ok","timestamp":1668144629227,"user_tz":0,"elapsed":144294,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["5077/5077 [==============================] - 130s 25ms/step\n"]}]},{"cell_type":"code","source":["matrix = confusion_matrix( y_test.argmax(axis=1), np.around(predictions, decimals=0).argmax(axis=1))\n","matrix"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4abqJvthKQAV","outputId":"90d3f290-79bc-45ec-f168-06f2efb513e2","executionInfo":{"status":"ok","timestamp":1668152397310,"user_tz":0,"elapsed":212,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[105458,   6797],\n","       [ 17196,  33003]])"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","source":["ax=sns.heatmap(matrix,annot=True,cmap='flare',fmt=' ')\n","ax.set_title('Confusion Matrix Best LSTM Model')\n","ax.set_xlabel('Actual')\n","ax.set_ylabel('Predicted')\n","plt.savefig('Best_LSTM_Model.png', dpi=1080, format='png')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"E6BHm3ksNbMQ","executionInfo":{"status":"ok","timestamp":1668144633646,"user_tz":0,"elapsed":4077,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}},"outputId":"74501055-8fcd-422c-c4c2-c85afc721d18"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["\n","# # visualisation: \n","# # name of classes \n","# class_names=[0,1] \n","# fig, ax = plt.subplots() \n","# tick_marks = np.arange(len(class_names)) \n","# plt.xticks(tick_marks, class_names) \n","# plt.yticks(tick_marks, class_names) \n","# # create heatmap \n","# sns.heatmap(pd.DataFrame(matrix), annot=True, cmap=\"viridis\" ,fmt='g') \n","# ax.xaxis.set_label_position(\"top\") \n","# plt.tight_layout() \n","# plt.title('Confusion matrix', y=1.1) \n","# plt.xlabel('Actual label')\n","# plt.ylabel('Predicted label') \n","# plt.savefig('LSTM_Model_CM.png', dpi=1080, format='png')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":325},"id":"4AOFYPodiM4y","executionInfo":{"status":"ok","timestamp":1667983141494,"user_tz":0,"elapsed":4935,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}},"outputId":"7112d410-256f-40fe-f8a0-e877a2dc3203"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["print(classification_report(y_test.argmax(axis=1), np.around(predictions, decimals=0).argmax(axis=1)))"],"metadata":{"id":"hHDHsv2pOgUU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668144633972,"user_tz":0,"elapsed":334,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}},"outputId":"6dd05334-7df9-4dc7-982a-5faa1e5f868d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.86      0.94      0.90    112255\n","           1       0.83      0.66      0.73     50199\n","\n","    accuracy                           0.85    162454\n","   macro avg       0.84      0.80      0.82    162454\n","weighted avg       0.85      0.85      0.85    162454\n","\n"]}]},{"cell_type":"code","source":["# sentiment = ['Negative','Positive']\n","# sequence = tokenizer.texts_to_sequences(['how given that the tories have not only crashed the economy forced bills to go up with that silly budget made inflation rise amp lowered living standards with pointless brexit amp created the costoflivingcrisis in so just how could labour be worse then them impossible'])\n","# test = pad_sequences(sequence, maxlen=max_len)\n","# sentiment[np.around(best_model.predict(test), decimals=0).argmax(axis=1)[0]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"OIfjU4U4OgCD","outputId":"fbb6737f-89e4-45d7-a6d8-e622a74fb332","executionInfo":{"status":"ok","timestamp":1667983318333,"user_tz":0,"elapsed":280,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 55ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["'Negative'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["# sentiment = ['Negative','Positive']\n","# sequence = tokenizer.texts_to_sequences(['I am sad'])\n","# test = pad_sequences(sequence, maxlen=max_len)\n","# sentiment[np.around(best_model.predict(test), decimals=0).argmax(axis=1)[0]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"0AjLpO4OjCr_","executionInfo":{"status":"ok","timestamp":1667983310437,"user_tz":0,"elapsed":241,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}},"outputId":"336ddb40-d3c4-435b-d279-dcf435911143"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 64ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["'Negative'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":["# sentiment = ['Negative','Positive']\n","# sequence = tokenizer.texts_to_sequences(['flying with @united is always a great experience'])\n","# test = pad_sequences(sequence, maxlen=max_len)\n","# sentiment[np.around(best_model.predict(test), decimals=0).argmax(axis=1)[0]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"kxNK-BGFP7aJ","outputId":"9fe44469-b8b2-47a3-bc48-663106e8421b","executionInfo":{"status":"ok","timestamp":1667983294842,"user_tz":0,"elapsed":537,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 101ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["'Positive'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["# sentiment = ['Negative','Positive']\n","# sequence = tokenizer.texts_to_sequences(['I am sick'])\n","# test = pad_sequences(sequence, maxlen=max_len)\n","# sentiment[np.around(best_model.predict(test), decimals=0).argmax(axis=1)[0]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"LwOC_R7UQFLh","outputId":"63456ba5-f725-4fe1-d59e-70c7188086b1","executionInfo":{"status":"ok","timestamp":1667983356515,"user_tz":0,"elapsed":8,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["'Negative'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":44}]},{"cell_type":"code","source":["# sentiment = ['Negative','Positive']\n","# sequence = tokenizer.texts_to_sequences(['this data science article is the best ever'])\n","# test = pad_sequences(sequence, maxlen=max_len)\n","# sentiment[np.around(best_model.predict(test), decimals=0).argmax(axis=1)[0]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"2wrhrgHtOfyD","outputId":"63c15b3d-5988-4db2-cdf0-a0b3dd88aecb","executionInfo":{"status":"ok","timestamp":1667983363996,"user_tz":0,"elapsed":248,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["'Negative'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["# sentiment = ['Negative','Positive']\n","# sequence = tokenizer.texts_to_sequences(['this is really sad'])\n","# test = pad_sequences(sequence, maxlen=max_len)\n","# sentiment[np.around(best_model.predict(test), decimals=0).argmax(axis=1)[0]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"KeiZUny1IzVj","outputId":"381f6964-f8ae-4f45-94cb-92efa4c66ba3","executionInfo":{"status":"ok","timestamp":1667983371804,"user_tz":0,"elapsed":232,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 61ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["'Negative'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":[],"metadata":{"id":"OYCjTNkhRB-v"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# testtweets= data[['Cleaned_tweets']]\n","# testtweets"],"metadata":{"id":"3v-yrUNBE3PG","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1668152498204,"user_tz":0,"elapsed":207,"user":{"displayName":"Frances Wireko","userId":"14106139935587129886"}},"outputId":"d014ca25-7114-4bc0-a211-93e8eed156d5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                           Cleaned_tweets\n","0       cost living crisis political choice yet tory t...\n","1       costoflivingcrisis stateimposedpoverty do quit...\n","2       every non tory mp stand today call borisjohnso...\n","3       ian blackford own show always tory care costof...\n","4       serious question scottish power say energy gre...\n","...                                                   ...\n","541723    honestly think liztruss plan cost living crisis\n","541724  every hour people die poverty uk rise costofli...\n","541725  jesus billion excess profit yrs time take stre...\n","541726  attend final leadership debate uk husting fell...\n","541727     borisjohnson plan visit site great achievement\n","\n","[541512 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-f45bf232-3083-425d-b024-e74fca763250\">\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>Cleaned_tweets</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>cost living crisis political choice yet tory t...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>costoflivingcrisis stateimposedpoverty do quit...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>every non tory mp stand today call borisjohnso...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ian blackford own show always tory care costof...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>serious question scottish power say energy gre...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>541723</th>\n","      <td>honestly think liztruss plan cost living crisis</td>\n","    </tr>\n","    <tr>\n","      <th>541724</th>\n","      <td>every hour people die poverty uk rise costofli...</td>\n","    </tr>\n","    <tr>\n","      <th>541725</th>\n","      <td>jesus billion excess profit yrs time take stre...</td>\n","    </tr>\n","    <tr>\n","      <th>541726</th>\n","      <td>attend final leadership debate uk husting fell...</td>\n","    </tr>\n","    <tr>\n","      <th>541727</th>\n","      <td>borisjohnson plan visit site great achievement</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>541512 rows × 1 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f45bf232-3083-425d-b024-e74fca763250')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 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