Sentiment_Analysis_using_NLP.ipynb

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          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import nltk\n",
        "import string\n",
        "import numpy as np\n",
        "import re\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords"
      ]
    },
    {
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      "source": [
        "data_set_url='/content/drive/MyDrive/Dataset/Tweets.csv' #Path to the data set\n",
        "data_set = pd.read_csv(data_set_url)\n",
        "data_set.head()"
      ],
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              "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
              "0  570306133677760513           neutral                        1.0000   \n",
              "1  570301130888122368          positive                        0.3486   \n",
              "2  570301083672813571           neutral                        0.6837   \n",
              "3  570301031407624196          negative                        1.0000   \n",
              "4  570300817074462722          negative                        1.0000   \n",
              "\n",
              "  negativereason  negativereason_confidence         airline  \\\n",
              "0            NaN                        NaN  Virgin America   \n",
              "1            NaN                     0.0000  Virgin America   \n",
              "2            NaN                        NaN  Virgin America   \n",
              "3     Bad Flight                     0.7033  Virgin America   \n",
              "4     Can't Tell                     1.0000  Virgin America   \n",
              "\n",
              "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
              "0                    NaN     cairdin                 NaN              0   \n",
              "1                    NaN    jnardino                 NaN              0   \n",
              "2                    NaN  yvonnalynn                 NaN              0   \n",
              "3                    NaN    jnardino                 NaN              0   \n",
              "4                    NaN    jnardino                 NaN              0   \n",
              "\n",
              "                                                text tweet_coord  \\\n",
              "0                @VirginAmerica What @dhepburn said.         NaN   \n",
              "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
              "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
              "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
              "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
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              "               tweet_created tweet_location               user_timezone  \n",
              "0  2015-02-24 11:35:52 -0800            NaN  Eastern Time (US & Canada)  \n",
              "1  2015-02-24 11:15:59 -0800            NaN  Pacific Time (US & Canada)  \n",
              "2  2015-02-24 11:15:48 -0800      Lets Play  Central Time (US & Canada)  \n",
              "3  2015-02-24 11:15:36 -0800            NaN  Pacific Time (US & Canada)  \n",
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        "data_set=data_set.drop(['tweet_id','airline_sentiment_confidence','negativereason','negativereason_confidence','airline','airline_sentiment_gold','name','negativereason_gold','retweet_count','tweet_coord','tweet_created','tweet_location','user_timezone'],axis=1)"
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              "  airline_sentiment                                               text\n",
              "0           neutral                @VirginAmerica What @dhepburn said.\n",
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              "2           neutral  @VirginAmerica I didn't today... Must mean I n...\n",
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              "    <div id=\"df-f7811313-5691-4393-a2b0-38b389928ac8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7811313-5691-4393-a2b0-38b389928ac8')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f7811313-5691-4393-a2b0-38b389928ac8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-51161e75-4538-4af8-aabe-8309fe369f32 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-51161e75-4538-4af8-aabe-8309fe369f32');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')\n",
        "ps = PorterStemmer()\n",
        "\n",
        "y = []\n",
        "\n",
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub(r'http.?://[^\\s]+[\\s]?', '', text)\n",
        "    text = nltk.word_tokenize(text)\n",
        "\n",
        "    for i in text:\n",
        "      if i not in stopwords.words('english'):\n",
        "        y.append(i)\n",
        "      text = y[:]\n",
        "      y.clear()\n",
        "\n",
        "    for i in text:\n",
        "        y.append(ps.stem(i))\n",
        "\n",
        "    return\" \".join(y)"
      ],
      "metadata": {
        "id": "V1TnABw24rCh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29977dfe-1a1a-47f9-cf0c-e40668e5b145"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text=data_set['text']\n",
        "\n",
        "for i in text:\n",
        "  clean_text(i)"
      ],
      "metadata": {
        "id": "U1A5Y8_B8bUk"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_cleaned_text_column(df):\n",
        "    data_set['cleaned_text'] = data_set['text'].apply(clean_text)\n",
        "    return data_set\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    data_set = add_cleaned_text_column(data_set)\n",
        "    print(data_set)"
      ],
      "metadata": {
        "id": "nDZqVpab-Xm-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da33dfdc-5a46-490b-ea36-33c3fe924ae0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      airline_sentiment                                               text  \\\n",
            "0               neutral                @VirginAmerica What @dhepburn said.   \n",
            "1              positive  @VirginAmerica plus you've added commercials t...   \n",
            "2               neutral  @VirginAmerica I didn't today... Must mean I n...   \n",
            "3              negative  @VirginAmerica it's really aggressive to blast...   \n",
            "4              negative  @VirginAmerica and it's a really big bad thing...   \n",
            "...                 ...                                                ...   \n",
            "14635          positive  @AmericanAir thank you we got on a different f...   \n",
            "14636          negative  @AmericanAir leaving over 20 minutes Late Flig...   \n",
            "14637           neutral  @AmericanAir Please bring American Airlines to...   \n",
            "14638          negative  @AmericanAir you have my money, you change my ...   \n",
            "14639           neutral  @AmericanAir we have 8 ppl so we need 2 know h...   \n",
            "\n",
            "       cleaned_text  \n",
            "0                 .  \n",
            "1                 .  \n",
            "2                 !  \n",
            "3           recours  \n",
            "4                    \n",
            "...             ...  \n",
            "14635             .  \n",
            "14636           svc  \n",
            "14637  blackberry10  \n",
            "14638             ?  \n",
            "14639             ?  \n",
            "\n",
            "[14640 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_cleaned = data_set['cleaned_text']\n",
        "tfidf_vectorizer = TfidfVectorizer(max_features=3000)\n",
        "tfidf_vectorizer.fit(text_cleaned)\n",
        "X = tfidf_vectorizer.transform(text_cleaned)\n",
        "X = X.toarray()\n",
        "Y = np.array(data_set['airline_sentiment'])"
      ],
      "metadata": {
        "id": "lrkMX1PXCRDI"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=.2,random_state=2)"
      ],
      "metadata": {
        "id": "YVOCR4OyvS4L"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "import sklearn.metrics\n",
        "clf = MultinomialNB()\n",
        "clf.fit(X, Y)\n",
        "predictions = clf.predict(x_test)\n"
      ],
      "metadata": {
        "id": "4MBbCxVh0T4x"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier()\n",
        "model.fit(x_train,y_train)\n",
        "y_pred = model.predict(X)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "id": "m9X65dSD0rl6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d44741ac-7c73-4503-d321-d618e561dcda"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['negative' 'negative' 'negative' ... 'neutral' 'negative' 'negative']\n"
          ]
        }
      ]
    }
  ]
}