CNN_Model (2).ipynb

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      "source": [
        "#import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow import keras\n",
        "import re\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Embedding, LSTM, SimpleRNN, SpatialDropout1D\n",
        "from keras.layers import Flatten, Conv1D, MaxPooling1D, Dropout, SpatialDropout1D, GlobalMaxPooling1D\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "-TjLD-zDYFCU"
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      "execution_count": null,
      "outputs": []
    },
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          "height": 38
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        "outputId": "4ca2053e-8723-49e7-8f75-6d4b23904f31"
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        "# Upload the CSV file\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
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              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
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      "cell_type": "code",
      "metadata": {
        "id": "OpMusXfQnI8u"
      },
      "source": [
        "data = pd.read_csv(\"Tweets.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "outputId": "c1b8bab6-2f27-4fff-e501-b7a92b7ffc0a"
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      "source": [
        "data = data[['text', 'airline_sentiment']]\n",
        "data.head()"
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      "execution_count": null,
      "outputs": [
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              "                                                text airline_sentiment\n",
              "0                @VirginAmerica What @dhepburn said.           neutral\n",
              "1  @VirginAmerica plus you've added commercials t...          positive\n",
              "2  @VirginAmerica I didn't today... Must mean I n...           neutral\n",
              "3  @VirginAmerica it's really aggressive to blast...          negative\n",
              "4  @VirginAmerica and it's a really big bad thing...          negative"
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              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
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              "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
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              "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
              "      <td>neutral</td>\n",
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              "      <th>3</th>\n",
              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
              "      <td>negative</td>\n",
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              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "#excluding neutral tweets to be (tow-class) task\n",
        "data = data[data['airline_sentiment'] != 'neutral']"
      ],
      "metadata": {
        "id": "gtn2A81lYYXE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tweets Pre-processing "
      ],
      "metadata": {
        "id": "k6GzT5NYYiVZ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "def clean_train_data(x):\n",
        "    text = x\n",
        "    text = text.lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text) # remove square brackets\n",
        "    text = re.sub(r'[^\\w\\s]','',text) # remove punctuation\n",
        "    text = re.sub('\\w*\\d\\w*', '', text) # remove words containing numbers\n",
        "    text = re.sub(r'\\s+', ' ', text, flags=re.I) # Substituting multiple spaces with single space\n",
        "    text = re.sub('\\n', '', text)\n",
        "    return text"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "id": "wvdTG_uRPnts",
        "outputId": "5538af6a-0ae3-4308-9593-3cd2a33857e3"
      },
      "source": [
        "data['text'] = data.text.apply(lambda x : clean_train_data(x))\n",
        "data.head()"
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      "execution_count": null,
      "outputs": [
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              "                                                text airline_sentiment\n",
              "0                   virginamerica what dhepburn said           neutral\n",
              "1  virginamerica plus youve added commercials to ...          positive\n",
              "2  virginamerica i didnt today must mean i need t...           neutral\n",
              "3  virginamerica its really aggressive to blast o...          negative\n",
              "4  virginamerica and its a really big bad thing a...          negative"
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              "      <td>virginamerica i didnt today must mean i need t...</td>\n",
              "      <td>neutral</td>\n",
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              "      <th>3</th>\n",
              "      <td>virginamerica its really aggressive to blast o...</td>\n",
              "      <td>negative</td>\n",
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              "      <td>virginamerica and its a really big bad thing a...</td>\n",
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              "    }\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-93c66291-87be-40a2-b5e7-247d8b95c292 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-93c66291-87be-40a2-b5e7-247d8b95c292');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_A307QmyTYl",
        "outputId": "9184cdd1-95d8-4f48-bea1-f5a122daac08"
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6Y97mE7j9Si"
      },
      "source": [
        "#Remove stopwords \n",
        "stop = set (stopwords.words (\"english\"))\n",
        "def remove_stopwords (text): \n",
        "  text = [word.lower () for word in text.split() if word.lower() not in stop]\n",
        "  return \" \".join(text)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qEcUvRRlSEN"
      },
      "source": [
        "data[\"text\"] = data[\"text\"].map(remove_stopwords)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_ICzUuWlhf6",
        "outputId": "ce636195-d822-4388-c932-15943784998f"
      },
      "source": [
        "data[\"text\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1        virginamerica plus youve added commercials exp...\n",
              "3        virginamerica really aggressive blast obnoxiou...\n",
              "4                       virginamerica really big bad thing\n",
              "5        virginamerica seriously would pay flight seats...\n",
              "6        virginamerica yes nearly every time fly vx ear...\n",
              "                               ...                        \n",
              "14633    americanair flight cancelled flightled leaving...\n",
              "14634                         americanair right cue delays\n",
              "14635       americanair thank got different flight chicago\n",
              "14636    americanair leaving minutes late flight warnin...\n",
              "14638    americanair money change flight dont answer ph...\n",
              "Name: text, Length: 11541, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytF8wyVAy2Fy",
        "outputId": "4e06c623-5ed1-4069-e0d8-ee201c40debc"
      },
      "source": [
        "import nltk\n",
        "from nltk.tokenize import sent_tokenize\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('punkt') # one time execution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDAkimStOv7p"
      },
      "source": [
        "dataY = pd.get_dummies(data['airline_sentiment']).values\n",
        "dX_train, dX_test, dY_train, dY_test = train_test_split(data['text'],dataY, test_size = 0.3, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fZ5rsEnl30e"
      },
      "source": [
        "num_words=None\n",
        "tokenizer = Tokenizer(num_words, split=' ') \n",
        "tokenizer.fit_on_texts(data['text'].values) \n",
        "#vocab_length = len(tokenizer.word_index) + 1 \n",
        "X_train = tokenizer.texts_to_sequences(dX_train.values) \n",
        "X_train = pad_sequences(X_train)\n",
        "X_test = tokenizer.texts_to_sequences(dX_test.values) \n",
        "X_test = pad_sequences(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZMsa31VWYaP"
      },
      "source": [
        "#X_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2hz8qYFoMF6"
      },
      "source": [
        "#to show how each word assigns to value after tokenizer \n",
        "word_index = tokenizer.word_index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gdhwLlDL6o3-",
        "outputId": "81c6b13b-5596-4019-c67f-82376957a335"
      },
      "source": [
        "# download pretrained GloVe word embeddings\n",
        "! wget http://nlp.stanford.edu/data/glove.6B.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-09 17:46:05--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2022-08-09 17:46:06--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2022-08-09 17:46:06--  https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  5.09MB/s    in 2m 39s  \n",
            "\n",
            "2022-08-09 17:48:46 (5.17 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-Wanx3U7CLq",
        "outputId": "d7a17ea4-6c1b-4518-a17a-5b8049d3e3e5"
      },
      "source": [
        "! unzip glove*.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S27q8u73Vrh6"
      },
      "source": [
        "embeddings_dictionary = dict()\n",
        "embedding_dim = 300\n",
        "glove_file = open('glove.6B.300d.txt')\n",
        "for line in glove_file:\n",
        "    records = line.split()\n",
        "    word = records[0]\n",
        "    vector_dimensions = np.asarray(records[1:], dtype='float32')\n",
        "    embeddings_dictionary [word] = vector_dimensions\n",
        "glove_file.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHIsoQZnV01i"
      },
      "source": [
        "vocab_len = len(word_index) + 1 \n",
        "embedding_matrix = np.zeros((vocab_len, embedding_dim))\n",
        "for word, index in tokenizer.word_index.items():\n",
        "    embedding_vector = embeddings_dictionary.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vl0Ago7CqL_"
      },
      "source": [
        "from keras.metrics import Precision, Recall\n",
        "from keras.callbacks import Callback,ModelCheckpoint\n",
        "from keras.models import Sequential,load_model\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "import keras.backend as K"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def f1 (y_true, y_pred): #taken from old keras source code\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    f1_val = 2*(precision*recall)/(precision+recall+K.epsilon())\n",
        "    return f1_val"
      ],
      "metadata": {
        "id": "0OUTMYavFg4S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building CNN model"
      ],
      "metadata": {
        "id": "88yskz5xY6vH"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSPReTyZQKm0",
        "outputId": "d81af44a-161a-4a31-f628-b67e3727b40b"
      },
      "source": [
        "#createmodel \n",
        "max_len= 32\n",
        "model = Sequential()\n",
        "model.add(Embedding(vocab_len, 300, weights= [embedding_matrix],input_length=None, trainable=False))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv1D(128,kernel_size=3,padding='same',activation='relu',strides=1))\n",
        "model.add(GlobalMaxPooling1D())\n",
        "\n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(2,activation='softmax'))\n",
        "\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy', Precision(), Recall(), f1])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_2 (Embedding)     (None, None, 300)         3393000   \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, None, 300)         0         \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, None, 128)         115328    \n",
            "                                                                 \n",
            " global_max_pooling1d_2 (Glo  (None, 128)              0         \n",
            " balMaxPooling1D)                                                \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,516,714\n",
            "Trainable params: 123,714\n",
            "Non-trainable params: 3,393,000\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQPASCz9pQWM"
      },
      "source": [
        "#model training \n",
        "batch_size = 32\n",
        "model.fit(X_train, dY_train, epochs = 10, batch_size=batch_size, verbose = 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sxoDRWkTqpBs",
        "outputId": "924b4ce1-343e-44b9-a690-4a5e232fab01"
      },
      "source": [
        "#model testing\n",
        "validation_size = 1500\n",
        "\n",
        "X_validate = X_test[-validation_size:]\n",
        "Y_validate = dY_test[-validation_size:]\n",
        "X_test = X_test[:-validation_size]\n",
        "dY_test = dY_test[:-validation_size]\n",
        "score,acc, precision, recall, f1 = model.evaluate(X_test, dY_test, verbose = 2, batch_size = batch_size)\n",
        "print(\"score: %.2f\" % (score))\n",
        "print(\"acc: %.2f\" % (acc))\n",
        "print(\"precision: %.2f\" % (precision))\n",
        "print(\"recall: %.2f\" % (recall))\n",
        "print(\"f1: %.2f\" % (f1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 - 0s - loss: 0.2020 - accuracy: 0.9071 - precision_2: 0.9071 - recall_2: 0.9071 - f1: 0.9057 - 56ms/epoch - 4ms/step\n",
            "score: 0.20\n",
            "acc: 0.91\n",
            "precision: 0.91\n",
            "recall: 0.91\n",
            "f1: 0.91\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoyDDmWI46gx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3b78e6a-4d0f-4d80-cb80-94a124a03577"
      },
      "source": [
        "# Predictions\n",
        "predictions = model.predict(X_test)\n",
        "predictions = np.round(predictions).astype(int)\n",
        "predictions=np.argmax(predictions, axis=1, out=None)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 50) for input KerasTensor(type_spec=TensorSpec(shape=(None, 50), dtype=tf.float32, name='embedding_49_input'), name='embedding_49_input', description=\"created by layer 'embedding_49_input'\"), but it was called on an input with incompatible shape (None, 21).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIS5tQxJ7PUC",
        "outputId": "65d84932-5b58-4251-8792-6bf001ec92b4"
      },
      "source": [
        "len(dX_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3809"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H47bB33U48Qt"
      },
      "source": [
        "df_test = pd.DataFrame(dX_test[:-validation_size]).reindex()\n",
        "df_test[\"target\"] = predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSxoO3Ya7llw",
        "outputId": "445ef9b0-3430-4d3d-de85-25e133afe3a2"
      },
      "source": [
        "len(df_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2309"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95bJiL1x4_Lg",
        "outputId": "eb6f8f8a-cbce-41d2-c2f8-e123ac9785d1"
      },
      "source": [
        "df_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2309, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "CKjgZsBd5C47",
        "outputId": "1f5fd47b-901e-4a0d-db1d-86ce10d7e568"
      },
      "source": [
        "# Creating submission file \n",
        "#submission = pd.DataFrame( data['text'])\n",
        "df_test.to_csv('Tweets.csv', index=False)\n",
        "df_test.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11825</th>\n",
              "      <td>usairways charged flight cancelled flightled u...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8105</th>\n",
              "      <td>jetblue great flight great view</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1279</th>\n",
              "      <td>united theyre actually gate agent rude standin...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14343</th>\n",
              "      <td>americanair worries called back hrs late fligh...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3829</th>\n",
              "      <td>united thank one months ago none weird dont cl...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2590</th>\n",
              "      <td>united brothers luggage lost copa airlines fli...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11912</th>\n",
              "      <td>americanair great thanks followed</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11544</th>\n",
              "      <td>usairways work hard making sure things flow sm...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3864</th>\n",
              "      <td>united told lostyou guys dont know</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7940</th>\n",
              "      <td>jetblue bein calling jetblue respond leave num...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1849</th>\n",
              "      <td>united got thanks</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1203</th>\n",
              "      <td>united please msg customer service invoice dry...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14593</th>\n",
              "      <td>americanair ahhhh silence golden tops anyone g...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5042</th>\n",
              "      <td>southwestair looks like running day still cant...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2984</th>\n",
              "      <td>united customer service terrible</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13550</th>\n",
              "      <td>americanair trying change flight hang ppl bc c...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>virginamerica looks like broken link assets ht...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>761</th>\n",
              "      <td>united worse cant figure pack plane screws con...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5069</th>\n",
              "      <td>southwestair thx customer service rep alex pat...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4637</th>\n",
              "      <td>southwestair people pre boarded needed wheel c...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text  target\n",
              "11825  usairways charged flight cancelled flightled u...       0\n",
              "8105                     jetblue great flight great view       1\n",
              "1279   united theyre actually gate agent rude standin...       0\n",
              "14343  americanair worries called back hrs late fligh...       0\n",
              "3829   united thank one months ago none weird dont cl...       0\n",
              "2590   united brothers luggage lost copa airlines fli...       0\n",
              "11912                  americanair great thanks followed       1\n",
              "11544  usairways work hard making sure things flow sm...       0\n",
              "3864                  united told lostyou guys dont know       0\n",
              "7940   jetblue bein calling jetblue respond leave num...       0\n",
              "1849                                   united got thanks       1\n",
              "1203   united please msg customer service invoice dry...       0\n",
              "14593  americanair ahhhh silence golden tops anyone g...       0\n",
              "5042   southwestair looks like running day still cant...       0\n",
              "2984                    united customer service terrible       0\n",
              "13550  americanair trying change flight hang ppl bc c...       0\n",
              "314    virginamerica looks like broken link assets ht...       0\n",
              "761    united worse cant figure pack plane screws con...       0\n",
              "5069   southwestair thx customer service rep alex pat...       0\n",
              "4637   southwestair people pre boarded needed wheel c...       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    }
  ]
}