08Sentiment_Analysis_ ml and dl.ipynb

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        "id": "14r3y0-DXBHv"
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      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.python.keras.layers import Dense, GRU, Embedding\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "import string\n",
        "from textblob import TextBlob\n",
        "import spacy\n",
        "nlp=spacy.load('en_core_web_sm')\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, precision_score,recall_score\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator"
      ]
    },
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      "source": [
        "def read_data():\n",
        "    dataset=pd.read_csv('/content/all-data.csv',\n",
        "                header=None,\n",
        "                 names=['Sentiment','News'],\n",
        "                 encoding=\"cp1252\")\n",
        "    return dataset\n",
        "\n",
        "def convert_to_lower_case():\n",
        "    def lower(input_text):\n",
        "        return input_text.lower()\n",
        "    dataset['News']=dataset['News'].apply(lower)\n",
        "    \n",
        "def remove_punctuation():\n",
        "    def remove_punctuation_from_text(input_text):\n",
        "        output_list=[word for word in input_text.split() if word.isalpha()]\n",
        "        return ' '.join(output_list)    \n",
        "    dataset['News']=dataset['News'].apply(remove_punctuation_from_text)\n",
        "\n",
        "def correct_words():\n",
        "    def correct_text(input_text):\n",
        "        list_1=[str(TextBlob(word).correct()) for word in input_text.split()]\n",
        "        output_text= ' '.join(list_1)\n",
        "        return output_text\n",
        "    dataset['News']=dataset['News'].apply(correct_text)\n",
        "    \n",
        "def lemmatize():\n",
        "    def lematize_text(input_text):\n",
        "        doc=nlp(input_text)\n",
        "        lemmas=[token.lemma_ for token in doc]\n",
        "        output_text=' '.join(lemmas)\n",
        "        return output_text\n",
        "    dataset['News']=dataset['News'].apply(lematize_text)\n",
        "    \n",
        "def remove_stopwords():\n",
        "    def remove_stopwords_from_text(input_text):\n",
        "        stopwords=spacy.lang.en.stop_words.STOP_WORDS\n",
        "        output_list=[word for word in input_text.split() if word not in stopwords and not(word=='-PRON-') ]\n",
        "        return ' '.join(output_list)\n",
        "    dataset['News']=dataset['News'].apply(remove_stopwords_from_text)\n",
        "\n",
        "def split_train_test():\n",
        "    data = dataset['News'].values.tolist()\n",
        "    target = dataset['Sentiment'].values.tolist()\n",
        "    x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42,stratify=target)\n",
        "    y_train=np.array(y_train)\n",
        "    y_test=np.array(y_test)\n",
        "    return x_train, x_test, y_train, y_test "
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      "source": [
        "#reading the data\n",
        "dataset=read_data()\n",
        "\n",
        "dataset.head()"
      ],
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      "execution_count": 99,
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          "data": {
            "text/plain": [
              "  Sentiment                                               News\n",
              "0   neutral  According to Gran , the company has no plans t...\n",
              "1   neutral  Technopolis plans to develop in stages an area...\n",
              "2  negative  The international electronic industry company ...\n",
              "3  positive  With the new production plant the company woul...\n",
              "4  positive  According to the company 's updated strategy f..."
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>News</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>According to Gran , the company has no plans t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>neutral</td>\n",
              "      <td>Technopolis plans to develop in stages an area...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>negative</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>positive</td>\n",
              "      <td>With the new production plant the company woul...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>positive</td>\n",
              "      <td>According to the company 's updated strategy f...</td>\n",
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      "source": [
        "dataset = dataset.replace('positive', 1)\n",
        "print(dataset.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGcnuoLEIyaP",
        "outputId": "3f02290f-f444-4d93-b54b-3cb7d81b4ce2"
      },
      "execution_count": 100,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Sentiment                                               News\n",
            "0   neutral  According to Gran , the company has no plans t...\n",
            "1   neutral  Technopolis plans to develop in stages an area...\n",
            "2  negative  The international electronic industry company ...\n",
            "3         1  With the new production plant the company woul...\n",
            "4         1  According to the company 's updated strategy f...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.replace('negative', 2)\n",
        "dataset = dataset.replace('neutral', 0)"
      ],
      "metadata": {
        "id": "Z5YjkRvOI-iL"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.head())"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "LaNQDtXFJUpE",
        "outputId": "d94df88e-934c-410a-d25a-2a1dddf4b5cf"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Sentiment                                               News\n",
            "0          0  According to Gran , the company has no plans t...\n",
            "1          0  Technopolis plans to develop in stages an area...\n",
            "2          2  The international electronic industry company ...\n",
            "3          1  With the new production plant the company woul...\n",
            "4          1  According to the company 's updated strategy f...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualizations**"
      ],
      "metadata": {
        "id": "dCHXt7D5wo9g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Sentiment'].value_counts().plot(kind='bar')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "Y6eaKhocwX1G",
        "outputId": "af692040-98ac-4e92-9e6e-59bd5c979b32"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/AjyUZPCnX/5yaUa1dHx6Z0iS/CdweVX97/PqlwBHq2r90oxMFyrJf1fVa5d6HJpekgeBt1fVs0lGga8CX6qqv07y/ap6y5IOcMic3hmeXwCvAZ54Xn1Va9MLWJIHZmoCVg5zLJq3l52b0qmqY0muBL6a5HVMfX5dMfSH58PAoSSP8csfnXst8AbgpiUbleZqJXA18PTz6gH+dfjD0Tw8mWRjVd0P0K74/xDYDfze0g5t+Az9IamqbyV5I1M/Lz14I/dwVZ1dupFpjr4BvPJccAxK8i/DH47m4XrgzGChqs4A1yf526UZ0tJxTl+SOuLTO5LUEUNfkjpi6EtSRwx9SeqIoS9JHfk/8VDYYM5vk9UAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "neutral_dataframe = dataset[dataset['Sentiment']==0]\n",
        "positive_dataframe = dataset[dataset['Sentiment']==1]\n",
        "negative_dataframe = dataset[dataset['Sentiment']==2]\n",
        "negative_dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zMQeniod03Sr",
        "outputId": "1f4c0cd0-0bd0-4fd4-ae38-b13303520119"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Sentiment                                               News\n",
              "2            2  The international electronic industry company ...\n",
              "415          2  A tinyurl link takes users to a scamming site ...\n",
              "421          2  Compared with the FTSE 100 index , which rose ...\n",
              "423          2  Compared with the FTSE 100 index , which rose ...\n",
              "500          2  One of the challenges in the oil production in..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a266aacd-25bd-45cb-aeb5-df71535d7b69\">\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>Sentiment</th>\n",
              "      <th>News</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>2</td>\n",
              "      <td>A tinyurl link takes users to a scamming site ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421</th>\n",
              "      <td>2</td>\n",
              "      <td>Compared with the FTSE 100 index , which rose ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>2</td>\n",
              "      <td>Compared with the FTSE 100 index , which rose ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>2</td>\n",
              "      <td>One of the challenges in the oil production in...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a266aacd-25bd-45cb-aeb5-df71535d7b69')\"\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-a266aacd-25bd-45cb-aeb5-df71535d7b69 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-a266aacd-25bd-45cb-aeb5-df71535d7b69');\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": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Pre-Processing**"
      ],
      "metadata": {
        "id": "EmAPzUnI2KsY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Preprocessing the text\n",
        "convert_to_lower_case()\n",
        "remove_punctuation()\n",
        "lemmatize()\n",
        "remove_stopwords()\n",
        "\n",
        "#Preparing data for model\n",
        "print(dataset.head())\n"
      ],
      "metadata": {
        "id": "kB7ePdnFwTkz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3464d38-1c9d-4ac4-b1ec-d636e1cff35c"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Sentiment                                               News\n",
            "0          0  accord gran company plan production russia com...\n",
            "1          0  technopoli plan develop stage area square mete...\n",
            "2          2  international electronic industry company elco...\n",
            "3          1  new production plant company increase capacity...\n",
            "4          1  accord company update strategy year basware ta...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGzDQDE5D8CA",
        "outputId": "9c54e6b8-0b77-4c02-b14e-d94e8df2197e"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Sentiment', 'News'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test =split_train_test()"
      ],
      "metadata": {
        "id": "D6cYrDqZHp89"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Training**"
      ],
      "metadata": {
        "id": "vZ1jUuq83-Ox"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**N-Grams, Decision Tree Classifier**"
      ],
      "metadata": {
        "id": "roT8qFP34Ei3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vect = CountVectorizer(min_df= 5, ngram_range=(1,2)).fit(x_train)"
      ],
      "metadata": {
        "id": "KgOs-H2z4HC3"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(vect.get_feature_names())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xD3fZMPs5Hqm",
        "outputId": "62ee0559-a738-4f28-8086-79dd8c66e733"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1936"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_vectorized = vect.transform(x_train)"
      ],
      "metadata": {
        "id": "fEd1I6hl5KqW"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_vectorized)"
      ],
      "metadata": {
        "id": "K4iWLAp55SoY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "595997aa-8e88-4f6b-e421-6d42bee4d54a"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 75)\t1\n",
            "  (0, 77)\t1\n",
            "  (0, 143)\t1\n",
            "  (0, 244)\t1\n",
            "  (0, 266)\t1\n",
            "  (0, 281)\t1\n",
            "  (0, 299)\t1\n",
            "  (0, 374)\t1\n",
            "  (0, 483)\t1\n",
            "  (0, 801)\t1\n",
            "  (0, 807)\t1\n",
            "  (0, 974)\t1\n",
            "  (0, 982)\t1\n",
            "  (0, 1040)\t1\n",
            "  (0, 1041)\t2\n",
            "  (0, 1046)\t1\n",
            "  (0, 1190)\t1\n",
            "  (0, 1235)\t1\n",
            "  (0, 1238)\t1\n",
            "  (0, 1379)\t1\n",
            "  (0, 1482)\t1\n",
            "  (0, 1539)\t1\n",
            "  (0, 1923)\t1\n",
            "  (1, 94)\t1\n",
            "  (1, 173)\t1\n",
            "  :\t:\n",
            "  (3873, 173)\t1\n",
            "  (3873, 443)\t1\n",
            "  (3873, 506)\t1\n",
            "  (3873, 509)\t2\n",
            "  (3873, 537)\t1\n",
            "  (3873, 1011)\t1\n",
            "  (3873, 1326)\t1\n",
            "  (3873, 1635)\t1\n",
            "  (3873, 1857)\t1\n",
            "  (3873, 1923)\t1\n",
            "  (3874, 94)\t1\n",
            "  (3874, 160)\t1\n",
            "  (3874, 424)\t1\n",
            "  (3874, 666)\t1\n",
            "  (3874, 784)\t1\n",
            "  (3874, 916)\t1\n",
            "  (3874, 1353)\t1\n",
            "  (3874, 1391)\t1\n",
            "  (3875, 37)\t1\n",
            "  (3875, 500)\t1\n",
            "  (3875, 501)\t1\n",
            "  (3875, 634)\t1\n",
            "  (3875, 1169)\t1\n",
            "  (3875, 1265)\t1\n",
            "  (3875, 1796)\t1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "DT = DecisionTreeClassifier()\n",
        "DT.fit(X_train_vectorized, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvUJEV5qKpYk",
        "outputId": "90d351a2-c284-4ed6-a45f-db7e0b6607a4"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_vectorized = vect.transform(x_test)"
      ],
      "metadata": {
        "id": "ZghIfg6YLRQT"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DT_predictions = DT.predict(X_test_vectorized)\n",
        "DT_score = DT.score(X_test_vectorized, y_test)\n",
        "print(DT_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYGMk-jmKpgB",
        "outputId": "bd9c0bfa-16f7-4516-d695-9d8e9d38183b"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7082474226804124\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "metadata": {
        "id": "cuFcrCH4iHp1"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, DT_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCuRslzIiU3m",
        "outputId": "f67ca9af-1eac-47ae-a0f0-76137cef7785"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.81      0.79       576\n",
            "           1       0.65      0.57      0.61       273\n",
            "           2       0.53      0.54      0.53       121\n",
            "\n",
            "    accuracy                           0.71       970\n",
            "   macro avg       0.65      0.64      0.64       970\n",
            "weighted avg       0.70      0.71      0.71       970\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm=confusion_matrix(y_test, DT_predictions,normalize='true')\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_fvuFJH1DNh",
        "outputId": "09545115-5962-439c-b81b-b48f12c14fc6"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.80902778, 0.11805556, 0.07291667],\n",
              "       [0.36996337, 0.57142857, 0.05860806],\n",
              "       [0.33057851, 0.1322314 , 0.53719008]])"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "classes=['neutral','positive','negative']\n",
        "df_cm=pd.DataFrame(cm,index=classes,columns=classes)\n",
        "ax=sn.heatmap(df_cm,annot=True,fmt='g')\n",
        "ax.set_xlabel(\"predicted\")\n",
        "ax.set_ylabel(\"target\")\n",
        "plt.title('Decision Tree classifier accuracy 70% ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "d3qt_7Ue1gIq",
        "outputId": "148d1d62-71fd-427a-e53f-c93b9f539ea7"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Decision Tree classifier accuracy 70% ')"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "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": [
        "print(DT_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LLK0yvGLsmy",
        "outputId": "34a4fb31-a3d3-4299-9c5a-6a764125a604"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 0 0 2 0 0 0 0 2 2 0 0 0 0 1 0 0 1 2 1 0 0 1 2 2 0 0 0 0 0 0 0 0 0 1 0 0\n",
            " 0 1 2 2 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 2 0 1 1 0 0 1\n",
            " 0 0 0 1 0 1 1 0 1 2 2 1 0 1 0 0 0 1 2 0 0 1 0 0 0 0 2 0 0 1 2 0 0 0 0 2 0\n",
            " 0 1 1 1 0 0 1 2 0 0 0 2 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 2 2 1 2 0 0 0 1 1 0\n",
            " 0 1 0 0 1 2 0 0 0 2 1 2 0 1 0 0 0 1 0 0 1 0 0 0 0 2 0 1 0 0 1 1 1 0 0 1 0\n",
            " 0 1 0 0 0 2 1 1 1 0 2 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0\n",
            " 0 0 0 0 0 2 0 2 1 0 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 2 0 0 1 0 0 1 1\n",
            " 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 2 0 1 1 1 0 1 0 1 2 0 0 1 1\n",
            " 0 1 1 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 2 0 1 0 0 0 1 0 0 0 2 0 2 0 2 1 0 0 0 0 1 2 0 0 0 0 0 1 2 0 0 0 1 0 1\n",
            " 0 1 1 0 0 0 2 2 2 0 0 0 0 2 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 1 0 2 2 0 2 0\n",
            " 0 0 0 1 0 1 2 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 0 2 1 0 0 0 1 0 0 0 0\n",
            " 0 0 0 0 0 0 0 2 0 1 2 0 1 1 0 1 0 0 0 0 2 1 0 1 1 0 1 2 2 0 0 2 1 0 1 0 0\n",
            " 0 0 1 0 2 0 0 0 1 0 1 1 2 0 0 0 0 0 0 2 0 1 0 1 1 0 0 0 0 2 2 0 2 1 1 0 0\n",
            " 0 0 1 0 0 0 1 0 1 0 0 0 1 2 2 1 0 0 2 0 0 0 0 1 0 0 0 0 0 1 0 0 0 2 0 2 0\n",
            " 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 1 0 2 0 0 1 1 0 0 0 2 0 0 0 0 0 0 1\n",
            " 2 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 2 0 0 0 0 2 1 0 0 2 2 0 1 2 0 0 1 2 0 1\n",
            " 1 0 2 0 0 0 1 0 0 0 1 0 2 1 0 0 2 0 0 1 0 1 1 1 0 0 0 1 0 0 2 0 1 0 0 0 0\n",
            " 0 0 1 0 0 1 2 0 0 1 2 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 2 1 2 2 1 0 1 0 1 1\n",
            " 1 0 0 0 0 1 0 2 0 0 0 0 0 0 2 0 0 0 0 0 2 2 1 0 0 0 2 0 0 0 0 1 0 1 1 2 0\n",
            " 1 2 0 0 0 0 0 1 1 0 1 1 1 2 1 0 0 0 0 1 0 2 0 0 1 0 1 2 0 2 1 0 1 0 1 0 1\n",
            " 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 0 1 0 0 1 0 0 0 2 0 0 1 1 1\n",
            " 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 1 0 0 1 1 0 1 2 1 0 1 0 0\n",
            " 0 1 1 2 2 1 0 0 0 0 1 0 2 0 0 1 0 0 2 0 1 0 0 1 2 0 0 1 0 0 0 0 0 1 0 1 2\n",
            " 1 0 0 0 0 1 1 0 1 0 2 1 0 1 2 0 1 0 0 1 0 0 0 0 0 0 1 1 0 2 1 0 2 0 1 2 2\n",
            " 1 0 0 0 0 0 2 0 0 1 0 0 0 2 1 0 1 1 1 2 0 0 0 0 2 1 0 1 0 2 1 0 0 1 0 0 1\n",
            " 0 2 1 2 1 0 2 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRnfy4rqLv2C",
        "outputId": "3fae3a2c-1d87-480a-e119-22ca6480049b"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 2 0 0 2 0 0 0 0 1 0 0 1 2 0 1 0 1 0 2 1 0 0 0 0 1 0 0 0 1 0 0\n",
            " 1 1 1 0 0 2 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 2 0 0 2 0 2 2 1 0 0\n",
            " 1 0 0 1 0 1 0 1 1 0 2 1 0 1 1 0 0 1 2 0 0 1 0 0 2 0 2 0 0 1 0 0 0 0 0 1 0\n",
            " 2 1 0 0 0 0 1 2 1 0 1 0 0 2 0 1 1 0 0 0 1 1 0 0 2 2 0 2 2 1 2 0 0 1 0 1 0\n",
            " 0 1 1 0 0 0 0 0 0 2 0 2 0 1 0 0 1 1 0 0 1 0 0 0 0 2 0 1 1 2 1 1 2 0 1 1 0\n",
            " 1 1 2 0 0 2 1 0 1 0 2 1 0 0 0 0 0 0 0 0 0 2 0 1 2 1 0 0 0 1 1 1 0 0 0 0 0\n",
            " 0 0 0 0 0 2 0 1 1 0 0 1 0 0 0 2 0 0 0 1 1 1 1 0 2 0 1 1 0 0 1 0 1 0 0 1 0\n",
            " 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 1 1 1 0 1 1 1 2 0 0 0 1\n",
            " 0 0 0 0 0 0 0 0 1 0 1 0 1 0 2 0 1 1 0 1 1 0 2 1 1 1 0 0 0 0 2 0 0 1 0 0 0\n",
            " 0 1 2 0 1 0 2 0 1 0 0 1 2 0 0 1 2 1 1 0 0 0 1 2 0 0 0 0 1 1 2 0 0 0 0 0 1\n",
            " 2 1 1 2 2 1 2 0 2 0 1 0 0 2 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0\n",
            " 0 1 2 1 0 0 0 1 0 2 0 0 0 1 0 1 0 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 2\n",
            " 0 0 1 0 0 0 0 1 0 1 0 2 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 2 2 2\n",
            " 0 0 1 0 2 0 0 1 1 0 1 1 2 2 1 0 0 0 0 1 1 1 2 0 1 0 0 0 0 0 2 1 0 1 0 0 0\n",
            " 1 0 1 0 1 0 1 1 1 2 0 0 1 2 2 1 0 0 0 1 0 0 2 1 0 1 0 0 0 0 0 0 0 2 0 1 0\n",
            " 0 2 1 1 0 0 2 0 1 0 1 2 0 0 0 0 2 0 0 0 0 0 1 0 1 1 0 2 0 0 0 0 0 0 0 0 1\n",
            " 0 1 2 0 0 0 0 0 0 0 1 0 0 0 1 1 0 2 0 0 0 0 0 0 1 1 2 2 2 1 2 0 0 1 0 0 1\n",
            " 0 0 2 0 0 0 1 0 0 0 1 0 0 1 1 0 1 1 2 0 0 0 1 2 2 0 0 0 0 0 2 0 1 0 0 2 0\n",
            " 1 0 1 1 0 1 2 1 0 0 2 1 0 0 0 0 0 0 0 0 0 2 1 0 0 0 1 1 1 2 1 2 0 0 2 1 1\n",
            " 1 0 0 0 0 2 0 0 0 0 0 0 1 0 2 0 0 0 0 0 2 1 1 0 0 0 2 1 0 1 0 1 0 0 1 0 0\n",
            " 1 2 0 0 0 0 0 1 1 0 0 1 1 0 1 0 2 1 0 1 0 2 0 0 1 1 0 2 1 0 1 2 1 2 1 0 0\n",
            " 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 2 1 0 1 2 0 0 1 1 2\n",
            " 0 0 0 0 0 2 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 2 0 1 0 0 1 0 0 1 2 1 0 1 0 0\n",
            " 0 2 1 0 1 0 0 1 0 0 1 0 2 0 1 0 0 0 2 0 0 2 0 1 2 1 0 1 1 0 0 0 1 1 0 0 2\n",
            " 1 0 0 0 0 1 0 0 0 0 1 0 0 1 2 1 1 1 0 0 0 0 0 0 0 0 1 0 0 2 0 1 2 0 1 2 1\n",
            " 0 0 1 1 0 0 2 0 1 0 0 0 0 2 1 1 1 1 1 1 0 0 0 0 2 1 0 1 0 0 1 2 1 1 0 0 1\n",
            " 0 0 0 0 1 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest Classifier**"
      ],
      "metadata": {
        "id": "CAXJ15ejMQ0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "I9a7ZmQ9ioVA"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RFC = RandomForestClassifier(n_estimators = 100)"
      ],
      "metadata": {
        "id": "BoghwDf8UWtV"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RFC.fit(X_train_vectorized, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHkjI30IUlF-",
        "outputId": "2971e171-9469-4cf7-a6cf-e42cf1330850"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RFC_predict = RFC.predict(X_test_vectorized)"
      ],
      "metadata": {
        "id": "3fEEno-CUofE"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RFC_score = RFC.score(X_test_vectorized, y_test)\n",
        "print(RFC_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFyk1naiUwiL",
        "outputId": "1d72c548-62a2-4341-ec12-dc8f51b23dfc"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7701030927835052\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, RFC_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cn_wEXNiM5cU",
        "outputId": "344c14ed-69d2-48b6-9773-5fc625f82a23"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.92      0.84       576\n",
            "           1       0.77      0.58      0.66       273\n",
            "           2       0.72      0.48      0.58       121\n",
            "\n",
            "    accuracy                           0.77       970\n",
            "   macro avg       0.76      0.66      0.69       970\n",
            "weighted avg       0.77      0.77      0.76       970\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "classes=['neutral','positive','negative']\n",
        "df_cm=pd.DataFrame(cm,index=classes,columns=classes)\n",
        "ax=sn.heatmap(df_cm,annot=True,fmt='g')\n",
        "ax.set_xlabel(\"predicted\")\n",
        "ax.set_ylabel(\"target\")\n",
        "plt.title('Random Forest classifier using countvectorizer accuracy 77% ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Rj_abco05ewg",
        "outputId": "76aa2cc1-9c0f-44b0-d7c9-c0eeea15a9db"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Random Forest classifier using countvectorizer accuracy 77% ')"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "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": "markdown",
      "source": [
        "**TF-IDF**"
      ],
      "metadata": {
        "id": "dXsTng-18OUd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf_vect = TfidfVectorizer().fit(x_train)"
      ],
      "metadata": {
        "id": "mfgFA9_p8Q-r"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_vectorized_tfidf = tfidf_vect.transform(x_train)"
      ],
      "metadata": {
        "id": "IfeSiIog8SBc"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RFC.fit(X_train_vectorized_tfidf, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e6d8868-bc67-4a62-fab8-37f053d5a414",
        "id": "fwZZigtwORdp"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RFC_predict = RFC.predict(tfidf_vect.transform(x_test))"
      ],
      "metadata": {
        "id": "YBC_vijfORdr"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RFC_score = RFC.score(tfidf_vect.transform(x_test), y_test)\n",
        "print(RFC_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7e8f1d8-9a49-4694-b181-b42c999539c1",
        "id": "pma-UQcyORdr"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7649484536082474\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, RFC_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8f9b420-3544-4c70-ec69-98c1732ad195",
        "id": "XKCvJ3cTORds"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.94      0.85       576\n",
            "           1       0.76      0.53      0.62       273\n",
            "           2       0.73      0.49      0.58       121\n",
            "\n",
            "    accuracy                           0.76       970\n",
            "   macro avg       0.75      0.65      0.68       970\n",
            "weighted avg       0.76      0.76      0.75       970\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "classes=['neutral','positive','negative']\n",
        "df_cm=pd.DataFrame(cm,index=classes,columns=classes)\n",
        "ax=sn.heatmap(df_cm,annot=True,fmt='g')\n",
        "ax.set_xlabel(\"predicted\")\n",
        "ax.set_ylabel(\"target\")\n",
        "plt.title('Decision Tree classifier using tf-idf accuracy 75% ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "h3HieBpp6cu3",
        "outputId": "18e85156-35f2-4070-9405-f83508fd1b94"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Decision Tree classifier using tf-idf accuracy 75% ')"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "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": [
        "DT.fit(X_train_vectorized_tfidf, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lM2ZxxLgOtGP",
        "outputId": "4e44acb7-abb7-410e-a719-f1509d53ebe0"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DT_predict = DT.predict(tfidf_vect.transform(x_test))"
      ],
      "metadata": {
        "id": "U14h6us9O4KG"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DT_score = RFC.score(tfidf_vect.transform(x_test), y_test)\n",
        "print(DT_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "824ee7db-e4dd-4203-f85f-cb31e2b97f8b",
        "id": "uwgYH17AO4KH"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7649484536082474\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, DT_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee02b027-fb54-4c4f-df20-66fbf1294503",
        "id": "n_NSBPo3O4KH"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.81      0.79       576\n",
            "           1       0.60      0.58      0.59       273\n",
            "           2       0.60      0.56      0.58       121\n",
            "\n",
            "    accuracy                           0.71       970\n",
            "   macro avg       0.66      0.65      0.65       970\n",
            "weighted avg       0.71      0.71      0.71       970\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "classes=['neutral','positive','negative']\n",
        "df_cm=pd.DataFrame(cm,index=classes,columns=classes)\n",
        "ax=sn.heatmap(df_cm,annot=True,fmt='g')\n",
        "ax.set_xlabel(\"predicted\")\n",
        "ax.set_ylabel(\"target\")\n",
        "plt.title('Random Forest classifier using tf-idf accuracy 79% ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "bBwyj3SG6Inj",
        "outputId": "5c3df67d-7be5-468a-a997-df8211f8aa83"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Random Forest classifier using tf-idf accuracy 79% ')"
            ]
          },
          "metadata": {},
          "execution_count": 139
        },
        {
          "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": "markdown",
      "source": [
        "**GRU**"
      ],
      "metadata": {
        "id": "NINTIAk_4BSH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_words=1000 # number of words that we are going to use. \n",
        "\n",
        "data = dataset['News'].values.tolist()\n",
        "\n",
        "tokenizer=Tokenizer(num_words=num_words)\n",
        "tokenizer.fit_on_texts(data)\n",
        "\n",
        "#tokenization\n",
        "x_train_tokens=tokenizer.texts_to_sequences(x_train)\n",
        "x_test_tokens=tokenizer.texts_to_sequences(x_test)\n",
        "\n",
        "\n",
        "#padding\n",
        "x_train_pad=pad_sequences(x_train_tokens)\n",
        "x_test_pad=pad_sequences(x_test_tokens)"
      ],
      "metadata": {
        "id": "RkfoMX5QY9Lh"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_pad.shape"
      ],
      "metadata": {
        "id": "BEW1IijmfYcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f406ca3d-b7ee-4cf2-97df-e31ea0f5800d"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3876, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "embedding_size=50  #create a 50 size vector for each word.\n",
        "model.add(Embedding(input_dim=num_words,\n",
        "                    output_dim=embedding_size,\n",
        "                    input_length=28,\n",
        "                    name='embedding_layer')) \n",
        "\n",
        "model.add(GRU(units=32,return_sequences=True))\n",
        "model.add(GRU(units=16,return_sequences=True) )\n",
        "model.add(GRU(units=8, return_sequences=True))\n",
        "model.add(GRU(units=4))\n",
        "model.add(Dense(3,activation='softmax'))\n",
        "optimizer=Adam(lr=1e-3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nc9X7o-tfbyn",
        "outputId": "7ce0f500-8e6c-4ac9-b2fd-99544aa43654"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=optimizer,\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "keYNpwYnfe2e"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.build(input_shape=(None,28))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8kFp5Aufh8W",
        "outputId": "325c24b1-39ba-44d9-c7d1-d0476d3de727"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " module_wrapper_15 (ModuleWr  (None, 28, 50)           50000     \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " module_wrapper_16 (ModuleWr  (None, 28, 32)           7968      \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " module_wrapper_17 (ModuleWr  (None, 28, 16)           2352      \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " module_wrapper_18 (ModuleWr  (None, 28, 8)            600       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " module_wrapper_19 (ModuleWr  (None, 4)                156       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " module_wrapper_20 (ModuleWr  (None, 3)                15        \n",
            " apper)                                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 61,091\n",
            "Trainable params: 61,091\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train_pad,\n",
        "          y_train,\n",
        "          epochs=10, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0ZXvSbAflAP",
        "outputId": "bb6475ea-94d0-40fc-9360-be9168e733e3"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "97/97 [==============================] - 21s 117ms/step - loss: 0.9465 - accuracy: 0.5955 - val_loss: 0.8526 - val_accuracy: 0.6392\n",
            "Epoch 2/10\n",
            "97/97 [==============================] - 10s 99ms/step - loss: 0.7888 - accuracy: 0.6758 - val_loss: 0.7424 - val_accuracy: 0.6997\n",
            "Epoch 3/10\n",
            "97/97 [==============================] - 9s 95ms/step - loss: 0.6103 - accuracy: 0.7826 - val_loss: 0.6824 - val_accuracy: 0.7229\n",
            "Epoch 4/10\n",
            "97/97 [==============================] - 10s 102ms/step - loss: 0.5049 - accuracy: 0.8310 - val_loss: 0.7020 - val_accuracy: 0.7281\n",
            "Epoch 5/10\n",
            "97/97 [==============================] - 10s 104ms/step - loss: 0.4472 - accuracy: 0.8565 - val_loss: 0.7059 - val_accuracy: 0.7268\n",
            "Epoch 6/10\n",
            "97/97 [==============================] - 11s 118ms/step - loss: 0.4034 - accuracy: 0.8748 - val_loss: 0.7246 - val_accuracy: 0.7191\n",
            "Epoch 7/10\n",
            "97/97 [==============================] - 9s 92ms/step - loss: 0.3704 - accuracy: 0.8890 - val_loss: 0.7489 - val_accuracy: 0.7126\n",
            "Epoch 8/10\n",
            "97/97 [==============================] - 10s 102ms/step - loss: 0.3410 - accuracy: 0.9016 - val_loss: 0.7802 - val_accuracy: 0.7191\n",
            "Epoch 9/10\n",
            "97/97 [==============================] - 10s 102ms/step - loss: 0.3230 - accuracy: 0.9071 - val_loss: 0.8028 - val_accuracy: 0.7139\n",
            "Epoch 10/10\n",
            "97/97 [==============================] - 10s 103ms/step - loss: 0.3139 - accuracy: 0.9094 - val_loss: 0.8291 - val_accuracy: 0.7113\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(history.history.keys())"
      ],
      "metadata": {
        "id": "NYWPYHm_hhbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c51eb746-95e1-41e4-a80e-4fe2a514214d"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "VPd3GXV4PeJc",
        "outputId": "ec75f385-8c7d-487e-8a6b-62b97a1a6ce3"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='lower right')\n",
        "plt.savefig('accuracy_epoch.png')\n",
        "plt.show()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "IjkFaRLrPkwJ",
        "outputId": "01a420e0-1141-4775-cfcb-712319112ecf"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper right')\n",
        "plt.savefig('loss_epoch.png')\n",
        "plt.show()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}