Emotion_Detection_on_Tweets (2).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install emoji==1.7\n",
        "import nltk\n",
        "nltk.download('omw-1.4')\n"
      ],
      "metadata": {
        "id": "5nzyZPBtHQMj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c039c44d-109c-4d19-a1e7-6aeb41c19211"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting emoji==1.7\n",
            "  Downloading emoji-1.7.0.tar.gz (175 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.4/175.4 KB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-1.7.0-py3-none-any.whl size=171046 sha256=d3a9762a4277308e836513138c6f30f7a1940510d6f8d7b1d050f8655acd6e72\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/8c/80/c3646df8201ba6f5070297fe3779a4b70265d0bfd961c15302\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-1.7.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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          "metadata": {},
          "execution_count": 1
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        "id": "h2heZbouemLj",
        "outputId": "c9665923-fe97-4150-9208-2724e32e57d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting text2emotion\n",
            "  Downloading text2emotion-0.0.5-py3-none-any.whl (57 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.8/57.8 KB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: nltk in /usr/local/lib/python3.8/dist-packages (from text2emotion) (3.7)\n",
            "Requirement already satisfied: emoji>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from text2emotion) (1.7.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from nltk->text2emotion) (7.1.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.8/dist-packages (from nltk->text2emotion) (2022.6.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from nltk->text2emotion) (4.64.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from nltk->text2emotion) (1.2.0)\n",
            "Installing collected packages: text2emotion\n",
            "Successfully installed text2emotion-0.0.5\n"
          ]
        }
      ],
      "source": [
        "#importing necessary libraries\n",
        "\n",
        "!pip install text2emotion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "pETGxLuqgZdC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8f9bfef-55f3-479b-c5ac-754458192fe7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "# imporitng the convertor\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# importing tabular data and matrices handlers\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# importing for calculations\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "\n",
        "#importing to ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "#importing for sentiment analysis\n",
        "import nltk\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "\n",
        "#importing classification models\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#importing for hyperparameter tuning\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "#importing visualization packages\n",
        "import seaborn as sns\n",
        "import matplotlib as mpl\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "#text to emotion \n",
        "import text2emotion as te\n",
        "\n",
        "#for evaluation\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score\n",
        "from sklearn import metrics\n",
        "\n",
        "%matplotlib inline\n",
        "mpl.style.use( 'ggplot' )\n",
        "plt.style.use('fivethirtyeight')\n",
        "sns.set(context=\"notebook\", palette=\"dark\", style = 'whitegrid' , color_codes=True)\n",
        "params = { \n",
        "    'axes.labelsize': \"large\",\n",
        "    'xtick.labelsize': 'x-large',\n",
        "    'legend.fontsize': 20,\n",
        "    'figure.dpi': 100,\n",
        "    'figure.figsize': [25, 7]\n",
        "}\n",
        "plt.rcParams.update(params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqjBVXshhGR1"
      },
      "source": [
        "**Loading the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "QEVkOYLsgZaH"
      },
      "outputs": [],
      "source": [
        "tweets_data = pd.read_csv('/content/drive/MyDrive/Sentiment Analysis/Electiondataset.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Eqzg7_cOgZXM",
        "outputId": "211fbbae-abdd-4c2b-b94f-ae1b107257fb"
      },
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
              "2  2023-01-21 10:30:54+00:00  1616745144585441283   \n",
              "3  2023-01-21 09:20:10+00:00  1616727344173178883   \n",
              "4  2023-01-21 00:06:02+00:00  1616587889865027589   \n",
              "\n",
              "                                                Text Username  \n",
              "0  He also urged the electorate in Khana to vote ...  gchahal  \n",
              "1  ...drugs tests are not required by Nigerian co...  gchahal  \n",
              "2  The NPC held a one-day roundtable meeting with...  gchahal  \n",
              "3  He described the reported endorsement as false...  gchahal  \n",
              "4  The governor said the incident was designed by...  gchahal  "
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              "      <td>gchahal</td>\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "tweets_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnEXlB0lkFJa"
      },
      "source": [
        "**Data Cleaning and Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OMFXRqr4gZUO"
      },
      "outputs": [],
      "source": [
        "# removing URL, special characters, extra spaces, etc\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'^b','') \n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'https?:\\/\\/.*\\/[a-zA-Z0-9]*', '') \n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'@[a-zA-Z0-9]{1,10}', '') \n",
        "\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\$[a-zA-Z0-9]*', '')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'[0-9]*','')\n",
        "\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\\\[a-z A-Z]{1,2}','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\:','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\\\n','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\#','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\/','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\'','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\\"','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\-','')\n",
        "\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\?','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\_','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'%','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\,','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'.','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\&amp','')\n",
        "\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r';','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'!','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\\\s','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\)','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\(','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\+','')\n",
        "tweets_data[\"Text\"]=tweets_data.Text.str.replace(r'\\=','')\n",
        "\n",
        "tweets_data['Text'] = tweets_data['Text'].str.strip().str.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
              "2  2023-01-21 10:30:54+00:00  1616745144585441283   \n",
              "3  2023-01-21 09:20:10+00:00  1616727344173178883   \n",
              "4  2023-01-21 00:06:02+00:00  1616587889865027589   \n",
              "\n",
              "                                                Text Username  \n",
              "0  he also urged the electorate in khana to vote ...  gchahal  \n",
              "1  drugs tests are not required by nigerian const...  gchahal  \n",
              "2  the npc held a oneday roundtable meeting with ...  gchahal  \n",
              "3  he described the reported endorsement as false...  gchahal  \n",
              "4  the governor said the incident was designed by...  gchahal  "
            ],
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              "      <td>gchahal</td>\n",
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              "      <td>2023-01-21 00:06:02+00:00</td>\n",
              "      <td>1616587889865027589</td>\n",
              "      <td>the governor said the incident was designed by...</td>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "tweets_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDUWkuxFm3GJ"
      },
      "source": [
        "**Analyzing Sentiment Intensity**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "1tOqY-WugZOX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dff3add-54b1-41b0-94f5-dbd1f6019464"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "#downloading vader_lexicon tool and initializing the sentiment analyzer\n",
        "nltk.download(\"vader_lexicon\")\n",
        "sentiment_analyzer = SentimentIntensityAnalyzer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "hcBPD7rpgZLQ"
      },
      "outputs": [],
      "source": [
        " #assigning polarities according to the text in tweets\n",
        "tweets_data[\"Positive\"] = [sentiment_analyzer.polarity_scores(itr)[\"pos\"] for itr in tweets_data[\"Text\"]]\n",
        "tweets_data[\"Negative\"] = [sentiment_analyzer.polarity_scores(itr)[\"neg\"] for itr in tweets_data[\"Text\"]]\n",
        "tweets_data[\"Neutral\"] = [sentiment_analyzer.polarity_scores(itr)[\"neu\"] for itr in tweets_data[\"Text\"]]\n",
        "tweets_data['Compound'] = [sentiment_analyzer.polarity_scores(itr)[\"compound\"] for itr in tweets_data[\"Text\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "hJ5RfKkBgZIp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "b9fadf0b-1af0-4931-bc88-37a191d7bd25"
      },
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          "data": {
            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
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              "4  2023-01-21 00:06:02+00:00  1616587889865027589   \n",
              "\n",
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              "\n",
              "   Negative  Neutral  Compound  \n",
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          "metadata": {},
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      ],
      "source": [
        "tweets_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "VVaXqEkSgZFn"
      },
      "outputs": [],
      "source": [
        "compound_values = tweets_data[\"Compound\"].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "owc5Al-ZgZC6"
      },
      "outputs": [],
      "source": [
        " #setting sentiments according to the compound polarity\n",
        "sentiment_list=[]\n",
        "for i in compound_values:\n",
        "    if i >= 0.05 :\n",
        "        sentiment_list.append('Positive')\n",
        "    elif i <= -0.05 :\n",
        "        sentiment_list.append('Negative')\n",
        "    else:\n",
        "        sentiment_list.append('Neutral')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "IbpqPHdVgY_0"
      },
      "outputs": [],
      "source": [
        "# getting unique values of sentiments\n",
        "list_set = set(sentiment_list)\n",
        "sentiment_list_unique = (list(list_set))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
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          "height": 424
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            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
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              "                                                Text Username  Positive  \\\n",
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              "3  he described the reported endorsement as false...  gchahal     0.055   \n",
              "4  the governor said the incident was designed by...  gchahal     0.122   \n",
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              "   Negative  Neutral  Compound Sentiment  \n",
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              "1     0.000    0.863    0.4019  Positive  \n",
              "2     0.000    0.917    0.4019  Positive  \n",
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              "      <th>0</th>\n",
              "      <td>2023-01-21 22:55:28+00:00</td>\n",
              "      <td>1616932519760519168</td>\n",
              "      <td>he also urged the electorate in khana to vote ...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-21 21:51:43+00:00</td>\n",
              "      <td>1616916475591397377</td>\n",
              "      <td>drugs tests are not required by nigerian const...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.137</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.863</td>\n",
              "      <td>0.4019</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-21 10:30:54+00:00</td>\n",
              "      <td>1616745144585441283</td>\n",
              "      <td>the npc held a oneday roundtable meeting with ...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.917</td>\n",
              "      <td>0.4019</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-21 09:20:10+00:00</td>\n",
              "      <td>1616727344173178883</td>\n",
              "      <td>he described the reported endorsement as false...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.034</td>\n",
              "      <td>0.911</td>\n",
              "      <td>0.2263</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-21 00:06:02+00:00</td>\n",
              "      <td>1616587889865027589</td>\n",
              "      <td>the governor said the incident was designed by...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.122</td>\n",
              "      <td>0.138</td>\n",
              "      <td>0.740</td>\n",
              "      <td>-0.1531</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37a8ceb4-f5d3-4b4f-ba49-730c72ff1a96')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-37a8ceb4-f5d3-4b4f-ba49-730c72ff1a96 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-37a8ceb4-f5d3-4b4f-ba49-730c72ff1a96');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# adding the sentiments to our initial dataframe\n",
        "tweets_data[\"Sentiment\"] = sentiment_list\n",
        "tweets_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "EPKzr2kRgY4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "d2bf1674-59df-437b-91a4-27117a4ad6c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        " #visualizing the count of each sentiment\n",
        "plt.style.use(\"seaborn\")\n",
        "fig, axes = plt.subplots()\n",
        "bp = sns.countplot(x=\"Sentiment\", data=tweets_data, palette=\"Set2\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbvwVFJQq3Yd",
        "outputId": "8a2f0571-3808-412d-dd46-db449c6c67e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(345, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "tweets_data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Emotion Detection"
      ],
      "metadata": {
        "id": "00sqpK8C9VFh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "-QbSbLyQCNeJ"
      },
      "outputs": [],
      "source": [
        "# copying the sentiment data into a new dataframe\n",
        "emotion_data = tweets_data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "75N6Z9IxCNYL"
      },
      "outputs": [],
      "source": [
        "# analyzing the emotions of tweets and adding them to the dataframe\n",
        "emotion_data[\"Emotion\"] = [te.get_emotion(iterator) for iterator in emotion_data['Text']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "niRfABUDCNVZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "outputId": "05efff33-ea5f-4fc7-bf70-ce2cff0c6a2a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
              "2  2023-01-21 10:30:54+00:00  1616745144585441283   \n",
              "3  2023-01-21 09:20:10+00:00  1616727344173178883   \n",
              "4  2023-01-21 00:06:02+00:00  1616587889865027589   \n",
              "\n",
              "                                                Text Username  Positive  \\\n",
              "0  he also urged the electorate in khana to vote ...  gchahal     0.000   \n",
              "1  drugs tests are not required by nigerian const...  gchahal     0.137   \n",
              "2  the npc held a oneday roundtable meeting with ...  gchahal     0.083   \n",
              "3  he described the reported endorsement as false...  gchahal     0.055   \n",
              "4  the governor said the incident was designed by...  gchahal     0.122   \n",
              "\n",
              "   Negative  Neutral  Compound Sentiment  \\\n",
              "0     0.000    1.000    0.0000   Neutral   \n",
              "1     0.000    0.863    0.4019  Positive   \n",
              "2     0.000    0.917    0.4019  Positive   \n",
              "3     0.034    0.911    0.2263  Positive   \n",
              "4     0.138    0.740   -0.1531  Negative   \n",
              "\n",
              "                                             Emotion  \n",
              "0  {'Happy': 1.0, 'Angry': 0.0, 'Surprise': 0.0, ...  \n",
              "1  {'Happy': 0.0, 'Angry': 0.0, 'Surprise': 0.5, ...  \n",
              "2  {'Happy': 0.0, 'Angry': 0.0, 'Surprise': 0.0, ...  \n",
              "3  {'Happy': 0.0, 'Angry': 0.0, 'Surprise': 0.0, ...  \n",
              "4  {'Happy': 0.0, 'Angry': 0.0, 'Surprise': 0.75,...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca89b7b5-0731-47ed-89a3-5221254e0667\">\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>Datetime</th>\n",
              "      <th>Tweet Id</th>\n",
              "      <th>Text</th>\n",
              "      <th>Username</th>\n",
              "      <th>Positive</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Compound</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-01-21 22:55:28+00:00</td>\n",
              "      <td>1616932519760519168</td>\n",
              "      <td>he also urged the electorate in khana to vote ...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>{'Happy': 1.0, 'Angry': 0.0, 'Surprise': 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-21 21:51:43+00:00</td>\n",
              "      <td>1616916475591397377</td>\n",
              "      <td>drugs tests are not required by nigerian const...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.137</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.863</td>\n",
              "      <td>0.4019</td>\n",
              "      <td>Positive</td>\n",
              "      <td>{'Happy': 0.0, 'Angry': 0.0, 'Surprise': 0.5, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-21 10:30:54+00:00</td>\n",
              "      <td>1616745144585441283</td>\n",
              "      <td>the npc held a oneday roundtable meeting with ...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.917</td>\n",
              "      <td>0.4019</td>\n",
              "      <td>Positive</td>\n",
              "      <td>{'Happy': 0.0, 'Angry': 0.0, 'Surprise': 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-21 09:20:10+00:00</td>\n",
              "      <td>1616727344173178883</td>\n",
              "      <td>he described the reported endorsement as false...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.034</td>\n",
              "      <td>0.911</td>\n",
              "      <td>0.2263</td>\n",
              "      <td>Positive</td>\n",
              "      <td>{'Happy': 0.0, 'Angry': 0.0, 'Surprise': 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-21 00:06:02+00:00</td>\n",
              "      <td>1616587889865027589</td>\n",
              "      <td>the governor said the incident was designed by...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>0.122</td>\n",
              "      <td>0.138</td>\n",
              "      <td>0.740</td>\n",
              "      <td>-0.1531</td>\n",
              "      <td>Negative</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "emotion_data.head()"
      ]
    },
    {
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              "     Happy  Angry  Surprise   Sad  Fear\n",
              "0      1.0    0.0      0.00  0.00  0.00\n",
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              "343    0.0    0.0      1.00  0.00  0.00\n",
              "344    0.0    0.0      0.20  0.20  0.60\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "# analyzing the weight of each emotion for each tweet\n",
        "emotion_dataframe = pd.DataFrame(emotion_data[\"Emotion\"].values.tolist(), index=emotion_data.index)\n",
        "emotion_dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "2I6VplqaCNNO"
      },
      "outputs": [],
      "source": [
        "# adding new emotions to our original dataframe and dropping the sentiments\n",
        "emotion_data[\"Happy\"] = emotion_dataframe[\"Happy\"]\n",
        "emotion_data.drop('Emotion', axis=1, inplace=True)\n",
        "emotion_data.drop(['Positive','Negative', 'Neutral', 'Compound'], axis=1, inplace=True)"
      ]
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    {
      "cell_type": "code",
      "execution_count": 22,
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            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
              "2  2023-01-21 10:30:54+00:00  1616745144585441283   \n",
              "3  2023-01-21 09:20:10+00:00  1616727344173178883   \n",
              "4  2023-01-21 00:06:02+00:00  1616587889865027589   \n",
              "\n",
              "                                                Text Username Sentiment  Happy  \n",
              "0  he also urged the electorate in khana to vote ...  gchahal   Neutral    1.0  \n",
              "1  drugs tests are not required by nigerian const...  gchahal  Positive    0.0  \n",
              "2  the npc held a oneday roundtable meeting with ...  gchahal  Positive    0.0  \n",
              "3  he described the reported endorsement as false...  gchahal  Positive    0.0  \n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "emotion_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "TnctKm3-CNCE"
      },
      "outputs": [],
      "source": [
        "# adding the rest of the emotions\n",
        "emotion_data[\"Angry\"] = emotion_dataframe[\"Angry\"]\n",
        "emotion_data[\"Surprise\"] = emotion_dataframe[\"Surprise\"]\n",
        "emotion_data[\"Sad\"] = emotion_dataframe[\"Sad\"]\n",
        "emotion_data[\"Fear\"] = emotion_dataframe[\"Fear\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
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        "id": "ye07Aks0CM9V",
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          "height": 337
        },
        "outputId": "184babe9-e389-42fd-d73e-10c24172f5a8"
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        {
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          "data": {
            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
              "2  2023-01-21 10:30:54+00:00  1616745144585441283   \n",
              "3  2023-01-21 09:20:10+00:00  1616727344173178883   \n",
              "4  2023-01-21 00:06:02+00:00  1616587889865027589   \n",
              "\n",
              "                                                Text Username Sentiment  \\\n",
              "0  he also urged the electorate in khana to vote ...  gchahal   Neutral   \n",
              "1  drugs tests are not required by nigerian const...  gchahal  Positive   \n",
              "2  the npc held a oneday roundtable meeting with ...  gchahal  Positive   \n",
              "3  he described the reported endorsement as false...  gchahal  Positive   \n",
              "4  the governor said the incident was designed by...  gchahal  Negative   \n",
              "\n",
              "   Happy  Angry  Surprise   Sad  Fear  \n",
              "0    1.0    0.0      0.00  0.00  0.00  \n",
              "1    0.0    0.0      0.50  0.00  0.50  \n",
              "2    0.0    0.0      0.00  1.00  0.00  \n",
              "3    0.0    0.0      0.00  0.25  0.75  \n",
              "4    0.0    0.0      0.75  0.25  0.00  "
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "emotion_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizing the emotion dataframe\n",
        "emotion_data[:5].plot(x=\"Tweet Id\", y=[\"Happy\", \"Angry\",\"Surprise\",\"Sad\",\"Fear\"], kind=\"bar\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "MTN21cmnEKev",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "4e3f2754-1d46-4526-9c8b-03e5b964ec47"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# deciding the emotion of each tweet by selecting emotion with maximum weight\n",
        "emotion_data['Emotion'] = emotion_data[[\"Happy\", \"Angry\",\"Surprise\",\"Sad\",\"Fear\"]].idxmax(axis=1)"
      ],
      "metadata": {
        "id": "i0xSY8cVEKcL"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotion_data.head()"
      ],
      "metadata": {
        "id": "kiEm1WE1EKW7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "73e7bb42-b149-46e4-9ede-f0d824c4a46a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Datetime             Tweet Id  \\\n",
              "0  2023-01-21 22:55:28+00:00  1616932519760519168   \n",
              "1  2023-01-21 21:51:43+00:00  1616916475591397377   \n",
              "2  2023-01-21 10:30:54+00:00  1616745144585441283   \n",
              "3  2023-01-21 09:20:10+00:00  1616727344173178883   \n",
              "4  2023-01-21 00:06:02+00:00  1616587889865027589   \n",
              "\n",
              "                                                Text Username Sentiment  \\\n",
              "0  he also urged the electorate in khana to vote ...  gchahal   Neutral   \n",
              "1  drugs tests are not required by nigerian const...  gchahal  Positive   \n",
              "2  the npc held a oneday roundtable meeting with ...  gchahal  Positive   \n",
              "3  he described the reported endorsement as false...  gchahal  Positive   \n",
              "4  the governor said the incident was designed by...  gchahal  Negative   \n",
              "\n",
              "   Happy  Angry  Surprise   Sad  Fear   Emotion  \n",
              "0    1.0    0.0      0.00  0.00  0.00     Happy  \n",
              "1    0.0    0.0      0.50  0.00  0.50  Surprise  \n",
              "2    0.0    0.0      0.00  1.00  0.00       Sad  \n",
              "3    0.0    0.0      0.00  0.25  0.75      Fear  \n",
              "4    0.0    0.0      0.75  0.25  0.00  Surprise  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f045e783-3761-4ea1-9591-97c20f9072cc\">\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>Datetime</th>\n",
              "      <th>Tweet Id</th>\n",
              "      <th>Text</th>\n",
              "      <th>Username</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Happy</th>\n",
              "      <th>Angry</th>\n",
              "      <th>Surprise</th>\n",
              "      <th>Sad</th>\n",
              "      <th>Fear</th>\n",
              "      <th>Emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-01-21 22:55:28+00:00</td>\n",
              "      <td>1616932519760519168</td>\n",
              "      <td>he also urged the electorate in khana to vote ...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Happy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-21 21:51:43+00:00</td>\n",
              "      <td>1616916475591397377</td>\n",
              "      <td>drugs tests are not required by nigerian const...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>Surprise</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-21 10:30:54+00:00</td>\n",
              "      <td>1616745144585441283</td>\n",
              "      <td>the npc held a oneday roundtable meeting with ...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Sad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-21 09:20:10+00:00</td>\n",
              "      <td>1616727344173178883</td>\n",
              "      <td>he described the reported endorsement as false...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>Fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-21 00:06:02+00:00</td>\n",
              "      <td>1616587889865027589</td>\n",
              "      <td>the governor said the incident was designed by...</td>\n",
              "      <td>gchahal</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Surprise</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f045e783-3761-4ea1-9591-97c20f9072cc')\"\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",
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              "      </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",
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              "      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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f045e783-3761-4ea1-9591-97c20f9072cc button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f045e783-3761-4ea1-9591-97c20f9072cc');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizing the count of each emotion\n",
        "bp = sns.countplot(x=\"Emotion\", data=emotion_data, palette=\"Set2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Atka0bSQEKSG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "efcd530b-4a3e-45c9-fae0-05d8f1127b54"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(10,5))\n",
        "total = float(len(emotion_data))\n",
        "ax = sns.countplot(x=\"Emotion\", hue=\"Emotion\", data=emotion_data)\n",
        "plt.title('Emotion_Analysis', fontsize=10)\n",
        "for p in ax.patches:\n",
        "    percentage = '{:.1f}%'.format(100 * p.get_height()/total)\n",
        "    x = p.get_x() + p.get_width()\n",
        "    y = p.get_height()\n",
        "    ax.annotate(percentage, (x, y),ha='center')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "rwpfnTyp_qQc",
        "outputId": "447cadd5-5850-45af-ed8f-570eed6d8a96"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# analyzing the corelation between our emotions and tweets\n",
        "emotion_data.corr()"
      ],
      "metadata": {
        "id": "Y57x-J51EKPc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "f77c778f-4bda-4afe-da95-4c0dd2366449"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Tweet Id     Happy     Angry  Surprise       Sad      Fear\n",
              "Tweet Id  1.000000 -0.015235  0.014664 -0.009262  0.062100  0.079673\n",
              "Happy    -0.015235  1.000000 -0.075286 -0.130480 -0.163218 -0.155098\n",
              "Angry     0.014664 -0.075286  1.000000 -0.105469 -0.071051 -0.056879\n",
              "Surprise -0.009262 -0.130480 -0.105469  1.000000 -0.115570 -0.248208\n",
              "Sad       0.062100 -0.163218 -0.071051 -0.115570  1.000000 -0.243795\n",
              "Fear      0.079673 -0.155098 -0.056879 -0.248208 -0.243795  1.000000"
            ],
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              "      <td>0.062100</td>\n",
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              "      <td>0.079673</td>\n",
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              "      <td>-0.056879</td>\n",
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              "    [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-98018e80-c184-40cd-ba32-b0a62485a37a 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-98018e80-c184-40cd-ba32-b0a62485a37a');\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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizing the corelation matrix\n",
        "sns.heatmap(emotion_data.corr(), square=True, cmap='nipy_spectral')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1mtatQY_EKL0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "22f40745-7350-4c02-9a7a-4aa8b383d725"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}