Emotion_Detection_in_Text.ipynb

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        "\n",
        "1.   Dataset\n",
        "2.   Understanding What’s Inside Your Dataset\n",
        "3.   Preprocessing\n",
        "4.   Train our Model\n",
        "5.   Model Testing\n",
        "6.   Hugging Face Transformers\n",
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        "drive.mount('/content/drive')"
      ],
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        "colab": {
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        },
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#%%\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "data = pd.read_csv('/content/drive/MyDrive/Courses /Data Science /NLP/Datasets/Emotion /text_emotion.csv')"
      ],
      "metadata": {
        "id": "JGsU4bSna0F7"
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      "execution_count": 2,
      "outputs": []
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              "     tweet_id   sentiment       author  \\\n",
              "0  1956967341       empty   xoshayzers   \n",
              "1  1956967666     sadness    wannamama   \n",
              "2  1956967696     sadness    coolfunky   \n",
              "3  1956967789  enthusiasm  czareaquino   \n",
              "4  1956968416     neutral    xkilljoyx   \n",
              "\n",
              "                                             content  \n",
              "0  @tiffanylue i know  i was listenin to bad habi...  \n",
              "1  Layin n bed with a headache  ughhhh...waitin o...  \n",
              "2                Funeral ceremony...gloomy friday...  \n",
              "3               wants to hang out with friends SOON!  \n",
              "4  @dannycastillo We want to trade with someone w...  "
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7e2f4627-3106-4064-abe4-e66f30bc4497 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-7e2f4627-3106-4064-abe4-e66f30bc4497');\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",
              "\n",
              "\n",
              "<div id=\"df-b19ac5d8-970c-4ddd-bc9d-d5e56cceb56b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b19ac5d8-970c-4ddd-bc9d-d5e56cceb56b')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b19ac5d8-970c-4ddd-bc9d-d5e56cceb56b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 40000,\n  \"fields\": [\n    {\n      \"column\": \"tweet_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118857896,\n        \"min\": 1693956175,\n        \"max\": 1966441171,\n        \"num_unique_values\": 40000,\n        \"samples\": [\n          1752414968,\n          1965295852,\n          1696219218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"relief\",\n          \"happiness\",\n          \"empty\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"author\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33871,\n        \"samples\": [\n          \"AmberLovesNKOTB\",\n          \"HeartPanda\",\n          \"deucehartley\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 39827,\n        \"samples\": [\n          \"the sun is shinning! im off out!!\",\n          \"wow i must have been tired. i fell asleep @ exactly the start of the 10pm news &amp; now up as if its 8am.\",\n          \"@knobzie their loss\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This one’s a simple dataset with just four columns, the tweet ID, emotion depicted by the tweet, the author, and the text content of the tweet. We do not necessarily need the author column. Hence we can drop it."
      ],
      "metadata": {
        "id": "qqYK3VrwbY75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop('author', axis=1)\n"
      ],
      "metadata": {
        "id": "XXcnN_YKbeoD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jCtM9xM2biP5",
        "outputId": "24ce090b-0beb-4040-b6be-8cd4fe681139"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tweet_id   sentiment                                            content\n",
              "0  1956967341       empty  @tiffanylue i know  i was listenin to bad habi...\n",
              "1  1956967666     sadness  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2  1956967696     sadness                Funeral ceremony...gloomy friday...\n",
              "3  1956967789  enthusiasm               wants to hang out with friends SOON!\n",
              "4  1956968416     neutral  @dannycastillo We want to trade with someone w..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb8068de-0298-4a28-9322-e6e587c10116\" 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>tweet_id</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>content</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1956967341</td>\n",
              "      <td>empty</td>\n",
              "      <td>@tiffanylue i know  i was listenin to bad habi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1956967666</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1956967696</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Funeral ceremony...gloomy friday...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1956967789</td>\n",
              "      <td>enthusiasm</td>\n",
              "      <td>wants to hang out with friends SOON!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1956968416</td>\n",
              "      <td>neutral</td>\n",
              "      <td>@dannycastillo We want to trade with someone w...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb8068de-0298-4a28-9322-e6e587c10116')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-eb8068de-0298-4a28-9322-e6e587c10116 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-eb8068de-0298-4a28-9322-e6e587c10116');\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",
              "\n",
              "\n",
              "<div id=\"df-98167059-5083-4bf0-afb1-e0fa1963fc5f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98167059-5083-4bf0-afb1-e0fa1963fc5f')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-98167059-5083-4bf0-afb1-e0fa1963fc5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 40000,\n  \"fields\": [\n    {\n      \"column\": \"tweet_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118857896,\n        \"min\": 1693956175,\n        \"max\": 1966441171,\n        \"num_unique_values\": 40000,\n        \"samples\": [\n          1752414968,\n          1965295852,\n          1696219218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"relief\",\n          \"happiness\",\n          \"empty\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 39827,\n        \"samples\": [\n          \"the sun is shinning! im off out!!\",\n          \"wow i must have been tired. i fell asleep @ exactly the start of the 10pm news &amp; now up as if its 8am.\",\n          \"@knobzie their loss\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset has 40,000 tweets in total, labeled into 13 different human sentiments. Our task here is to build a model, such that give a new tweet or text sentence, it can accurately identify which of the emotions (for which it is trained to recognize) it depicts. For this tutorial let us consider just two of these sentiments for simplicity, ‘happiness’ and ‘sadness’ (which constitute a total of about 10,000 tweets from the entire sample of data). We can thus drop rows with all other labels."
      ],
      "metadata": {
        "id": "WT8NS96FbquA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**3-Preprocessing**</p>"
      ],
      "metadata": {
        "id": "YEQLDjVkbtfX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping rows with other emotion labels\n"
      ],
      "metadata": {
        "id": "H4uegPzIb2wp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping rows with other emotion labels\n",
        "data = data.drop(data[data.sentiment == 'anger'].index)\n",
        "data = data.drop(data[data.sentiment == 'boredom'].index)\n",
        "data = data.drop(data[data.sentiment == 'enthusiasm'].index)\n",
        "data = data.drop(data[data.sentiment == 'empty'].index)\n",
        "data = data.drop(data[data.sentiment == 'fun'].index)\n",
        "data = data.drop(data[data.sentiment == 'relief'].index)\n",
        "data = data.drop(data[data.sentiment == 'surprise'].index)\n",
        "data = data.drop(data[data.sentiment == 'love'].index)\n",
        "data = data.drop(data[data.sentiment == 'hate'].index)\n",
        "data = data.drop(data[data.sentiment == 'neutral'].index)\n",
        "data = data.drop(data[data.sentiment == 'worry'].index)"
      ],
      "metadata": {
        "id": "oIz2OD8acBn1"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "df2y9cHMcHoB",
        "outputId": "5c81fdf1-88b5-41cd-aa94-4b2e499e9fd9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tweet_id sentiment                                            content\n",
              "1  1956967666   sadness  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2  1956967696   sadness                Funeral ceremony...gloomy friday...\n",
              "6  1956968487   sadness  I should be sleep, but im not! thinking about ...\n",
              "8  1956969035   sadness            @charviray Charlene my love. I miss you\n",
              "9  1956969172   sadness         @kelcouch I'm sorry  at least it's Friday?"
            ],
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "<style>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    }\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-395dc062-10d7-4bba-ad46-c9344deea57c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10374,\n  \"fields\": [\n    {\n      \"column\": \"tweet_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118670673,\n        \"min\": 1693956462,\n        \"max\": 1966440078,\n        \"num_unique_values\": 10374,\n        \"samples\": [\n          1963271422,\n          1752255578,\n          1965687736\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"happiness\",\n          \"sadness\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10353,\n        \"samples\": [\n          \"Bacon Fail at Gulb... lack of chef\",\n          \"just came back home from NE gig, another fun night skankin...what a workout!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Vislization**"
      ],
      "metadata": {
        "id": "JrrklMerkhQI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "e64eURHtlOhg"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['sentiment'].value_counts().plot.bar(color = 'pink', figsize = (6, 4))\n"
      ],
      "metadata": {
        "id": "1dztB9yyk9MS",
        "outputId": "10e9025e-f649-4efb-f811-f730b8e93bfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sentiment'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the distribution of tweets in the data\n",
        "\n",
        "length_train = data['content'].str.len().plot.hist(color = 'pink', figsize = (6, 4))\n"
      ],
      "metadata": {
        "id": "AZVWnL_OlWsr",
        "outputId": "7d50dedf-96d6-49ee-a586-76a5515a5a5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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99pkeeeQRRUZG6t5779XJkyfbqk4AAHCNu6yrb/bt26eXX35Z69evV3BwsB555BElJyfrxIkTWrRokSZOnMjXOgCAK4srr64ZrQoly5Yt05o1a1RcXKxx48bp1Vdf1bhx4+Tj882Bl+joaK1du1a9evVqy1oBAMA1rFWhZNWqVfrtb3+rX//61+rRo0eza8LDw/XSSy9dVnEAAKDjaFUoOXLkyEXX+Pv7KykpqTW7BwAAHVCrTnRds2aNNmzYcM74hg0b9Morr1x2UQAAoONpVShJS0tT9+7dzxkPDw/XM888c9lFAQCAjqdVoaSkpETR0dHnjPfs2VMlJSUt3k9ubq4mTJggp9Mpm82mzZs3e8wbhqGFCxeqR48eCgoKUnx8/DlfHVVUVCgxMVEhISEKDQ1VcnKyqqurPdbs379ft99+uwIDAxUZGan09PSWNwsAAK6KVoWS8PBw7d+//5zxjz76SN26dWvxfmpqajRo0CCtXLmy2fn09HRlZGQoMzNT+fn5Cg4OVkJCgset7RMTE3Xw4EFlZWVp69atys3N1YwZM8x5t9ut0aNHq2fPniooKNDSpUv15JNPavXq1ZfQMQAAuNJadaLrXXfdpYceekhdunTRiBEjJEk5OTmaOXOmpk2b1uL9jB07VmPHjm12zjAMLV++XI8//rgmTpwoSXr11VcVERGhzZs3a9q0aTp06JC2b9+uvXv3Kibmm+u/V6xYoXHjxunPf/6znE6n1q1bp7q6Or388svy9/dX//79VVRUpGXLlnmEl++qra1VbW2t+djtdre4LwAAcOladaTkqaeeUmxsrEaNGqWgoCAFBQVp9OjR+ulPf9pm55QcPXpULpdL8fHx5pjdbldsbKzy8vIkSXl5eQoNDTUDiSTFx8fLx8dH+fn55poRI0bI39/fXJOQkKDi4mKdOnXqvK+flpYmu91ubpGRkW3SFwAAaF6rQom/v7/eeOMNffrpp1q3bp02btyozz//3Dwa0RZcLpckKSIiwmM8IiLCnHO5XAoPD/eY9/X1VVhYmMea5vbx7ddozvz581VVVWVux48fv7yGAADABV3WbeZvvPFG3XjjjW1Vi6UEBAQoICDA22UAuNa0l1uiS9wWHVddq0JJQ0OD1q5dq+zsbJWXl6uxsdFjfseOHZddmMPhkCSVlZV53DW2rKxMgwcPNteUl5d7PO/s2bOqqKgwn+9wOFRWVuaxpulx0xoAAOB9rfr6ZubMmZo5c6YaGho0YMAADRo0yGNrC9HR0XI4HMrOzjbH3G638vPzFRcXJ0mKi4tTZWWlCgoKzDU7duxQY2OjYmNjzTW5ubmqr68312RlZemmm25S165d26RWAABw+Vp1pGT9+vX6+9//rnHjxl3Wi1dXV+uzzz4zHx89elRFRUUKCwtTVFSUZs2apaefflq9e/dWdHS0FixYIKfTqUmTJkmS+vbtqzFjxui+++5TZmam6uvrlZqaqmnTpsnpdEqS7r77bi1atEjJycmaN2+eDhw4oOeff17PPffcZdUOAADaVqtCib+/v374wx9e9ovv27dPI0eONB/PmTNHkpSUlKS1a9fq0UcfVU1NjWbMmKHKykoNHz5c27dvV2BgoPmcdevWKTU1VaNGjZKPj4+mTp2qjIwMc95ut+vtt99WSkqKhgwZou7du2vhwoUXvBwYAABcfTbDMIxLfdKzzz6rL774Qi+88IJsNtuVqMty3G637Ha7qqqqFBIS4u1yrr72dHIegLbRXk50bS/vT+3l3+cV0NLP0FYdKXnvvfe0c+dObdu2Tf3795efn5/H/MaNG1uzWwAA0IG1KpSEhoZq8uTJbV0LAADowFoVStasWdPWdQAAgA6uVZcES9/cD+Sdd97RX/7yF50+fVqSVFpaes4v9AIAALREq46UHDt2TGPGjFFJSYlqa2v1X//1X+rSpYuWLFmi2tpaZWZmtnWdAADgGtfqm6fFxMTo1KlTCgoKMscnT57scbMzAACAlmrVkZJ3331Xu3fvPufH93r16qUvv/yyTQoDAAAdS6tCSWNjoxoaGs4ZP3HihLp06XLZRQEALKC93P8D14xWfX0zevRoLV++3Hxss9lUXV2tJ5544rJvPQ8AADqmVh0pefbZZ5WQkKB+/frpzJkzuvvuu3XkyBF1795dr7/+elvXCAAAOoBWhZLrr79eH330kdavX6/9+/erurpaycnJSkxM9DjxFQAAoKVaFUokydfXV/fcc09b1gIAADqwVoWSV1999YLz9957b6uKAQAAHVerQsnMmTM9HtfX1+vrr7+Wv7+/OnfuTCgBAACXrFVX35w6dcpjq66uVnFxsYYPH86JrgAAoFVa/ds339W7d28tXrz4nKMoAAAALdFmoUT65uTX0tLSttwlAADoIFp1Tsmbb77p8dgwDJ08eVIvvPCChg0b1iaFAQCAjqVVoWTSpEkej202m773ve/ppz/9qZ599tm2qAsAAHQwrf7tGwAAgLbUpueUAAAAtFarjpTMmTOnxWuXLVvWmpcAAAAdTKtCyYcffqgPP/xQ9fX1uummmyRJhw8fVqdOnXTLLbeY62w2W9tUCQAArnmt+vpmwoQJGjFihE6cOKHCwkIVFhbq+PHjGjlypH72s59p586d2rlzp3bs2HFZxTU0NGjBggWKjo5WUFCQbrjhBj311FMyDMNcYxiGFi5cqB49eigoKEjx8fE6cuSIx34qKiqUmJiokJAQhYaGKjk5WdXV1ZdVGwAAaFutCiXPPvus0tLS1LVrV3Osa9euevrpp9v06pslS5Zo1apVeuGFF3To0CEtWbJE6enpWrFihbkmPT1dGRkZyszMVH5+voKDg5WQkKAzZ86YaxITE3Xw4EFlZWVp69atys3N1YwZM9qsTgAAcPla9fWN2+3WV199dc74V199pdOnT192UU12796tiRMnavz48ZKkXr166fXXX9eePXskfXOUZPny5Xr88cc1ceJESd/8WGBERIQ2b96sadOm6dChQ9q+fbv27t2rmJgYSdKKFSs0btw4/fnPf5bT6WyzegEAQOu16kjJ5MmT9Zvf/EYbN27UiRMndOLECf3P//yPkpOTNWXKlDYr7sc//rGys7N1+PBhSdJHH32k9957T2PHjpUkHT16VC6XS/Hx8eZz7Ha7YmNjlZeXJ0nKy8tTaGioGUgkKT4+Xj4+PsrPzz/va9fW1srtdntsAADgymnVkZLMzEw98sgjuvvuu1VfX//Njnx9lZycrKVLl7ZZcY899pjcbrf69OmjTp06qaGhQX/605+UmJgoSXK5XJKkiIgIj+dFRESYcy6XS+Hh4R7zvr6+CgsLM9c0Jy0tTYsWLWqzXgAAwIW1KpR07txZL774opYuXarPP/9cknTDDTcoODi4TYv7+9//rnXr1um1115T//79VVRUpFmzZsnpdCopKalNX+u75s+f73Hps9vtVmRk5BV9TQAAOrJWhZImJ0+e1MmTJzVixAgFBQXJMIw2vQx47ty5euyxxzRt2jRJ0sCBA3Xs2DGlpaUpKSlJDodDklRWVqYePXqYzysrK9PgwYMlSQ6HQ+Xl5R77PXv2rCoqKsznNycgIEABAQFt1gsAALiwVp1T8u9//1ujRo3SjTfeqHHjxunkyZOSpOTkZD388MNtVtzXX38tHx/PEjt16mTe5j46OloOh0PZ2dnmvNvtVn5+vuLi4iRJcXFxqqysVEFBgblmx44damxsVGxsbJvVCgAALk+rQsns2bPl5+enkpISde7c2Rz/1a9+pe3bt7dZcRMmTNCf/vQn/fOf/9T//u//atOmTVq2bJkmT54s6Zubs82aNUtPP/203nzzTX388ce699575XQ6zR8N7Nu3r8aMGaP77rtPe/bs0fvvv6/U1FRNmzaNK28AALCQVn198/bbb+utt97S9ddf7zHeu3dvHTt2rE0Kk765dHfBggX6/e9/r/LycjmdTv3ud7/TwoULzTWPPvqoampqNGPGDFVWVmr48OHavn27AgMDzTXr1q1TamqqRo0aJR8fH02dOlUZGRltVicAALh8NuPbt0dtoS5duqiwsFC9e/dWly5d9NFHH+kHP/iB9u3bp4SEBP373/++ErV6ldvtlt1uV1VVlUJCQrxdztWXs8/bFQBA+3ZHzMXXXKNa+hnaqq9vbr/9dr366qvmY5vNpsbGRqWnp2vkyJGt2SUAAOjgWvX1TXp6ukaNGqV9+/aprq5Ojz76qA4ePKiKigq9//77bV0jAADoAFp1pGTAgAE6fPiwhg8frokTJ6qmpkZTpkzRhx9+qBtuuKGtawQAAB3AJR8pqa+v15gxY5SZmak//OEPV6ImAADQAV3ykRI/Pz/t37//StQCAAA6sFZ9fXPPPffopZdeautaAABAB9aqE13Pnj2rl19+We+8846GDBlyzm/eLFu2rE2KAwAAHcclhZIvvvhCvXr10oEDB3TLLbdIkg4fPuyxpi1/+wYAAHQclxRKevfurZMnT2rnzp2SvrmtfEZGhiIiIq5IcQAAoOO4pHNKvnvz123btqmmpqZNCwIAAB1Tq050bdKKO9QDAAA065JCic1mO+ecEc4hAQAAbeGSzikxDEO//vWvFRAQIEk6c+aM7r///nOuvtm4cWPbVQgAADqESwolSUlJHo/vueeeNi0GAAB0XJcUStasWXOl6gAAAB3cZZ3oCgAA0FYIJQAAwBIIJQAAwBIIJQAAwBIIJQAAwBIIJQAAwBIIJQAAwBIIJQAAwBIsH0q+/PJL3XPPPerWrZuCgoI0cOBA7du3z5w3DEMLFy5Ujx49FBQUpPj4eB05csRjHxUVFUpMTFRISIhCQ0OVnJys6urqq90KAAC4AEuHklOnTmnYsGHy8/PTtm3b9Mknn+jZZ59V165dzTXp6enKyMhQZmam8vPzFRwcrISEBJ05c8Zck5iYqIMHDyorK0tbt25Vbm6uZsyY4Y2WAADAedgMwzC8XcT5PPbYY3r//ff17rvvNjtvGIacTqcefvhhPfLII5KkqqoqRUREaO3atZo2bZoOHTqkfv36ae/evYqJiZEkbd++XePGjdOJEyfkdDpbVIvb7ZbdbldVVZVCQkLapsH2JGffxdcAAM7vjhhvV+A1Lf0MtfSRkjfffFMxMTH6xS9+ofDwcN18883661//as4fPXpULpdL8fHx5pjdbldsbKzy8vIkSXl5eQoNDTUDiSTFx8fLx8dH+fn5533t2tpaud1ujw0AAFw5lg4lX3zxhVatWqXevXvrrbfe0gMPPKCHHnpIr7zyiiTJ5XJJkiIiIjyeFxERYc65XC6Fh4d7zPv6+iosLMxc05y0tDTZ7XZzi4yMbMvWAADAd1g6lDQ2NuqWW27RM888o5tvvlkzZszQfffdp8zMzCv+2vPnz1dVVZW5HT9+/Iq/JgAAHZmlQ0mPHj3Ur18/j7G+ffuqpKREkuRwOCRJZWVlHmvKysrMOYfDofLyco/5s2fPqqKiwlzTnICAAIWEhHhsAADgyrF0KBk2bJiKi4s9xg4fPqyePXtKkqKjo+VwOJSdnW3Ou91u5efnKy4uTpIUFxenyspKFRQUmGt27NihxsZGxcbGXoUuAABAS/h6u4ALmT17tn784x/rmWee0S9/+Uvt2bNHq1ev1urVqyVJNptNs2bN0tNPP63evXsrOjpaCxYskNPp1KRJkyR9c2RlzJgx5tc+9fX1Sk1N1bRp01p85Q0AALjyLB1Khg4dqk2bNmn+/Pn64x//qOjoaC1fvlyJiYnmmkcffVQ1NTWaMWOGKisrNXz4cG3fvl2BgYHmmnXr1ik1NVWjRo2Sj4+Ppk6dqoyMDG+0BAAAzsPS9ymxEu5Twn1KAOCycJ+S9n2fEgAA0HEQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCUQSgAAgCX4eruADi9nn7crAADAEjhSAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALKFdhZLFixfLZrNp1qxZ5tiZM2eUkpKibt266brrrtPUqVNVVlbm8bySkhKNHz9enTt3Vnh4uObOnauzZ89e5eoBAMCFtJtQsnfvXv3lL3/Rj370I4/x2bNna8uWLdqwYYNycnJUWlqqKVOmmPMNDQ0aP3686urqtHv3br3yyitau3atFi5ceLVbAAAAF9AuQkl1dbUSExP117/+VV27djXHq6qq9NJLL2nZsmX66U9/qiFDhmjNmjXavXu3PvjgA0nS22+/rU8++UR/+9vfNHjwYI0dO1ZPPfWUVq5cqbq6Om+1BAAAvqNdhJKUlBSNHz9e8fHxHuMFBQWqr6/3GO/Tp4+ioqKUl5cnScrLy9PAgQMVERFhrklISJDb7dbBgwfP+5q1tbVyu90eGwAAuHIsf5v59evXq7CwUHv37j1nzuVyyd/fX6GhoR7jERERcrlc5ppvB5Km+aa580lLS9OiRYsus3oAANBSlj5Scvz4cc2cOVPr1q1TYGDgVX3t+fPnq6qqytyOHz9+VV8fAICOxtKhpKCgQOXl5brlllvk6+srX19f5eTkKCMjQ76+voqIiFBdXZ0qKys9nldWViaHwyFJcjgc51yN0/S4aU1zAgICFBIS4rEBAIArx9KhZNSoUfr4449VVFRkbjExMUpMTDT/2c/PT9nZ2eZziouLVVJSori4OElSXFycPv74Y5WXl5trsrKyFBISon79+l31ngAAQPMsfU5Jly5dNGDAAI+x4OBgdevWzRxPTk7WnDlzFBYWppCQED344IOKi4vTbbfdJkkaPXq0+vXrp+nTpys9PV0ul0uPP/64UlJSFBAQcNV7AgAAzbN0KGmJ5557Tj4+Ppo6dapqa2uVkJCgF1980Zzv1KmTtm7dqgceeEBxcXEKDg5WUlKS/vjHP3qxagAA8F02wzAMbxfRHrjdbtntdlVVVbXt+SU5+9puXwAA67ojxtsVeE1LP0MtfU4JAADoOAglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEgglAADAEiwfStLS0jR06FB16dJF4eHhmjRpkoqLiz3WnDlzRikpKerWrZuuu+46TZ06VWVlZR5rSkpKNH78eHXu3Fnh4eGaO3euzp49ezVbAQAAF2D5UJKTk6OUlBR98MEHysrKUn19vUaPHq2amhpzzezZs7VlyxZt2LBBOTk5Ki0t1ZQpU8z5hoYGjR8/XnV1ddq9e7deeeUVrV27VgsXLvRGSwAAoBk2wzAMbxdxKb766iuFh4crJydHI0aMUFVVlb73ve/ptdde089//nNJ0qeffqq+ffsqLy9Pt912m7Zt26af/exnKi0tVUREhCQpMzNT8+bN01dffSV/f/+Lvq7b7ZbdbldVVZVCQkLarqGcfW23LwCAdd0R4+0KvKaln6GWP1LyXVVVVZKksLAwSVJBQYHq6+sVHx9vrunTp4+ioqKUl5cnScrLy9PAgQPNQCJJCQkJcrvdOnjwYLOvU1tbK7fb7bEBAIArp12FksbGRs2aNUvDhg3TgAEDJEkul0v+/v4KDQ31WBsRESGXy2Wu+XYgaZpvmmtOWlqa7Ha7uUVGRrZxNwAA4NvaVShJSUnRgQMHtH79+iv+WvPnz1dVVZW5HT9+/Iq/JgAAHZmvtwtoqdTUVG3dulW5ubm6/vrrzXGHw6G6ujpVVlZ6HC0pKyuTw+Ew1+zZs8djf01X5zSt+a6AgAAFBAS0cRcAAOB8LH+kxDAMpaamatOmTdqxY4eio6M95ocMGSI/Pz9lZ2ebY8XFxSopKVFcXJwkKS4uTh9//LHKy8vNNVlZWQoJCVG/fv2uTiMAAOCCLH+kJCUlRa+99pr+8Y9/qEuXLuY5IHa7XUFBQbLb7UpOTtacOXMUFhamkJAQPfjgg4qLi9Ntt90mSRo9erT69eun6dOnKz09XS6XS48//rhSUlI4GgIAgEVYPpSsWrVKkvSTn/zEY3zNmjX69a9/LUl67rnn5OPjo6lTp6q2tlYJCQl68cUXzbWdOnXS1q1b9cADDyguLk7BwcFKSkrSH//4x6vVBgAAuIh2d58Sb+E+JQCAy8J9Sq69+5QAAIBrE6EEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYAqEEAABYgq+3CwAAoEPI2eftClrmjhivvTRHSgAAgCV0qFCycuVK9erVS4GBgYqNjdWePXu8XRIAAPj/OkwoeeONNzRnzhw98cQTKiws1KBBg5SQkKDy8nJvlwYAACTZDMMwvF3E1RAbG6uhQ4fqhRdekCQ1NjYqMjJSDz74oB577LFz1tfW1qq2ttZ8XFVVpaioKB0/flwhISFtV9h7hW23LwAALtfwW9p8l263W5GRkaqsrJTdbj/vug5xomtdXZ0KCgo0f/58c8zHx0fx8fHKy8tr9jlpaWlatGjROeORkZFXrE4AAK5lp0+fJpT861//UkNDgyIiIjzGIyIi9Omnnzb7nPnz52vOnDnm48bGRlVUVKhbt26y2Wxm6mvzIycWcK32dq32JV27vV2rfUn01h5dq31JV743wzB0+vRpOZ3OC67rEKGkNQICAhQQEOAxFhoaes66kJCQa+4vZ5NrtbdrtS/p2u3tWu1Lorf26FrtS7qyvV3oCEmTDnGia/fu3dWpUyeVlZV5jJeVlcnhcHipKgAA8G0dIpT4+/tryJAhys7ONscaGxuVnZ2tuLg4L1YGAACadJivb+bMmaOkpCTFxMTo1ltv1fLly1VTU6Pf/OY3rdpfQECAnnjiiXO+4rkWXKu9Xat9Sddub9dqXxK9tUfXal+SdXrrMJcES9ILL7ygpUuXyuVyafDgwcrIyFBsbKy3ywIAAOpgoQQAAFhXhzinBAAAWB+hBAAAWAKhBAAAWAKhBAAAWAKhpJVWrlypXr16KTAwULGxsdqzZ4+3S7okaWlpGjp0qLp06aLw8HBNmjRJxcXFHmvOnDmjlJQUdevWTdddd52mTp16zg3orG7x4sWy2WyaNWuWOdae+/ryyy91zz33qFu3bgoKCtLAgQO1b98+c94wDC1cuFA9evRQUFCQ4uPjdeTIES9W3DINDQ1asGCBoqOjFRQUpBtuuEFPPfWUvn0efnvoLTc3VxMmTJDT6ZTNZtPmzZs95lvSQ0VFhRITExUSEqLQ0FAlJyerurr6KnbRvAv1Vl9fr3nz5mngwIEKDg6W0+nUvffeq9LSUo99WLG3i/2Zfdv9998vm82m5cuXe4xbsS+pZb0dOnRId955p+x2u4KDgzV06FCVlJSY81f7/ZJQ0gpvvPGG5syZoyeeeEKFhYUaNGiQEhISVF5e7u3SWiwnJ0cpKSn64IMPlJWVpfr6eo0ePVo1NTXmmtmzZ2vLli3asGGDcnJyVFpaqilTpnix6kuzd+9e/eUvf9GPfvQjj/H22tepU6c0bNgw+fn5adu2bfrkk0/07LPPqmvXruaa9PR0ZWRkKDMzU/n5+QoODlZCQoLOnDnjxcovbsmSJVq1apVeeOEFHTp0SEuWLFF6erpWrFhhrmkPvdXU1GjQoEFauXJls/Mt6SExMVEHDx5UVlaWtm7dqtzcXM2YMeNqtXBeF+rt66+/VmFhoRYsWKDCwkJt3LhRxcXFuvPOOz3WWbG3i/2ZNdm0aZM++OCDZn+7xYp9SRfv7fPPP9fw4cPVp08f7dq1S/v379eCBQsUGBhorrnq75cGLtmtt95qpKSkmI8bGhoMp9NppKWlebGqy1NeXm5IMnJycgzDMIzKykrDz8/P2LBhg7nm0KFDhiQjLy/PW2W22OnTp43evXsbWVlZxh133GHMnDnTMIz23de8efOM4cOHn3e+sbHRcDgcxtKlS82xyspKIyAgwHj99devRomtNn78eOO3v/2tx9iUKVOMxMREwzDaZ2+SjE2bNpmPW9LDJ598Ykgy9u7da67Ztm2bYbPZjC+//PKq1X4x3+2tOXv27DEkGceOHTMMo330dr6+Tpw4YXz/+983Dhw4YPTs2dN47rnnzLn20JdhNN/br371K+Oee+4573O88X7JkZJLVFdXp4KCAsXHx5tjPj4+io+PV15enhcruzxVVVWSpLCwMElSQUGB6uvrPfrs06ePoqKi2kWfKSkpGj9+vEf9Uvvu680331RMTIx+8YtfKDw8XDfffLP++te/mvNHjx6Vy+Xy6M1utys2Ntbyvf34xz9Wdna2Dh8+LEn66KOP9N5772ns2LGS2ndvTVrSQ15enkJDQxUTE2OuiY+Pl4+Pj/Lz8696zZejqqpKNpvN/CHT9tpbY2Ojpk+frrlz56p///7nzLfnvv75z3/qxhtvVEJCgsLDwxUbG+vxFY833i8JJZfoX//6lxoaGhQREeExHhERIZfL5aWqLk9jY6NmzZqlYcOGacCAAZIkl8slf3//c34ZuT30uX79ehUWFiotLe2cufbc1xdffKFVq1apd+/eeuutt/TAAw/ooYce0iuvvCJJZv3t8e/mY489pmnTpqlPnz7y8/PTzTffrFmzZikxMVFS++6tSUt6cLlcCg8P95j39fVVWFhYu+lT+uY8hHnz5umuu+4yf3G2vfa2ZMkS+fr66qGHHmp2vr32VV5erurqai1evFhjxozR22+/rcmTJ2vKlCnKycmR5J33yw7z2zc4v5SUFB04cEDvvfeet0u5bMePH9fMmTOVlZXl8b3otaCxsVExMTF65plnJEk333yzDhw4oMzMTCUlJXm5usvz97//XevWrdNrr72m/v37q6ioSLNmzZLT6Wz3vXU09fX1+uUvfynDMLRq1Spvl3NZCgoK9Pzzz6uwsFA2m83b5bSpxsZGSdLEiRM1e/ZsSdLgwYO1e/duZWZm6o477vBKXRwpuUTdu3dXp06dzjn7uKysTA6Hw0tVtV5qaqq2bt2qnTt36vrrrzfHHQ6H6urqVFlZ6bHe6n0WFBSovLxct9xyi3x9feXr66ucnBxlZGTI19dXERER7bIvSerRo4f69evnMda3b1/zTPmm+tvj3825c+eaR0sGDhyo6dOna/bs2ebRrvbcW5OW9OBwOM45Yf7s2bOqqKhoF302BZJjx44pKyvLPEoitc/e3n33XZWXlysqKsp8Pzl27Jgefvhh9erVS1L77Ev65rPM19f3ou8pV/v9klByifz9/TVkyBBlZ2ebY42NjcrOzlZcXJwXK7s0hmEoNTVVmzZt0o4dOxQdHe0xP2TIEPn5+Xn0WVxcrJKSEkv3OWrUKH388ccqKioyt5iYGCUmJpr/3B77kqRhw4adc9n24cOH1bNnT0lSdHS0HA6HR29ut1v5+fmW7+3rr7+Wj4/n21GnTp3M/5trz701aUkPcXFxqqysVEFBgblmx44damxstPyPhzYFkiNHjuidd95Rt27dPObbY2/Tp0/X/v37Pd5PnE6n5s6dq7feektS++xL+uazbOjQoRd8T/HK58AVOX32Grd+/XojICDAWLt2rfHJJ58YM2bMMEJDQw2Xy+Xt0lrsgQceMOx2u7Fr1y7j5MmT5vb111+ba+6//34jKirK2LFjh7Fv3z4jLi7OiIuL82LVrfPtq28Mo/32tWfPHsPX19f405/+ZBw5csRYt26d0blzZ+Nvf/ubuWbx4sVGaGio8Y9//MPYv3+/MXHiRCM6Otr4z3/+48XKLy4pKcn4/ve/b2zdutU4evSosXHjRqN79+7Go48+aq5pD72dPn3a+PDDD40PP/zQkGQsW7bM+PDDD80rUFrSw5gxY4ybb77ZyM/PN9577z2jd+/exl133eWtlkwX6q2urs648847jeuvv94oKiryeE+pra0192HF3i72Z/Zd3736xjCs2ZdhXLy3jRs3Gn5+fsbq1auNI0eOGCtWrDA6depkvPvuu+Y+rvb7JaGklVasWGFERUUZ/v7+xq233mp88MEH3i7pkkhqdluzZo255j//+Y/x+9//3ujatavRuXNnY/LkycbJkye9V3QrfTeUtOe+tmzZYgwYMMAICAgw+vTpY6xevdpjvrGx0ViwYIERERFhBAQEGKNGjTKKi4u9VG3Lud1uY+bMmUZUVJQRGBho/OAHPzD+8Ic/eHygtYfedu7c2ex/V0lJSYZhtKyHf//738Zdd91lXHfddUZISIjxm9/8xjh9+rQXuvF0od6OHj163veUnTt3mvuwYm8X+zP7ruZCiRX7MoyW9fbSSy8ZP/zhD43AwEBj0KBBxubNmz32cbXfL22G8a1bJgIAAHgJ55QAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABL+H+3nRMeg5fMVwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ENajPT9JmnRx",
        "outputId": "a12976e6-93b7-44fc-d984-76750a2b8aaf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tweet_id sentiment                                            content\n",
              "1  1956967666   sadness  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2  1956967696   sadness                Funeral ceremony...gloomy friday...\n",
              "6  1956968487   sadness  I should be sleep, but im not! thinking about ...\n",
              "8  1956969035   sadness            @charviray Charlene my love. I miss you\n",
              "9  1956969172   sadness         @kelcouch I'm sorry  at least it's Friday?"
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>content</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1956967666</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1956967696</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Funeral ceremony...gloomy friday...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1956968487</td>\n",
              "      <td>sadness</td>\n",
              "      <td>I should be sleep, but im not! thinking about ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1956969035</td>\n",
              "      <td>sadness</td>\n",
              "      <td>@charviray Charlene my love. I miss you</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1956969172</td>\n",
              "      <td>sadness</td>\n",
              "      <td>@kelcouch I'm sorry  at least it's Friday?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26ad3b69-8514-48e8-9651-5d835ce02966')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-26ad3b69-8514-48e8-9651-5d835ce02966 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-26ad3b69-8514-48e8-9651-5d835ce02966');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2a723198-2cb9-48ad-abb6-f9d20ba1fa82')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2a723198-2cb9-48ad-abb6-f9d20ba1fa82 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10374,\n  \"fields\": [\n    {\n      \"column\": \"tweet_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118670673,\n        \"min\": 1693956462,\n        \"max\": 1966440078,\n        \"num_unique_values\": 10374,\n        \"samples\": [\n          1963271422,\n          1752255578,\n          1965687736\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"happiness\",\n          \"sadness\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10353,\n        \"samples\": [\n          \"Bacon Fail at Gulb... lack of chef\",\n          \"just came back home from NE gig, another fun night skankin...what a workout!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# adding a column to represent the length of the tweet\n",
        "\n",
        "data['len'] = data['content'].str.len()"
      ],
      "metadata": {
        "id": "DMixI_XGmVHi"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Q2_rymQimwcS",
        "outputId": "68b67015-0611-4d84-cdb1-cc4a619e6469"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tweet_id sentiment                                            content  \\\n",
              "1  1956967666   sadness  Layin n bed with a headache  ughhhh...waitin o...   \n",
              "2  1956967696   sadness                Funeral ceremony...gloomy friday...   \n",
              "6  1956968487   sadness  I should be sleep, but im not! thinking about ...   \n",
              "8  1956969035   sadness            @charviray Charlene my love. I miss you   \n",
              "9  1956969172   sadness         @kelcouch I'm sorry  at least it's Friday?   \n",
              "\n",
              "   len  \n",
              "1   60  \n",
              "2   35  \n",
              "6  132  \n",
              "8   39  \n",
              "9   42  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1439619b-6d1d-4a1c-b47d-a0e6837b592e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>1</th>\n",
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              "      <td>sadness</td>\n",
              "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1956967696</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Funeral ceremony...gloomy friday...</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1956968487</td>\n",
              "      <td>sadness</td>\n",
              "      <td>I should be sleep, but im not! thinking about ...</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1956969035</td>\n",
              "      <td>sadness</td>\n",
              "      <td>@charviray Charlene my love. I miss you</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1956969172</td>\n",
              "      <td>sadness</td>\n",
              "      <td>@kelcouch I'm sorry  at least it's Friday?</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1439619b-6d1d-4a1c-b47d-a0e6837b592e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1439619b-6d1d-4a1c-b47d-a0e6837b592e 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-1439619b-6d1d-4a1c-b47d-a0e6837b592e');\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",
              "\n",
              "\n",
              "<div id=\"df-963d38b3-83f4-475b-89d1-79aebef33d15\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-963d38b3-83f4-475b-89d1-79aebef33d15')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-963d38b3-83f4-475b-89d1-79aebef33d15 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10374,\n  \"fields\": [\n    {\n      \"column\": \"tweet_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118670673,\n        \"min\": 1693956462,\n        \"max\": 1966440078,\n        \"num_unique_values\": 10374,\n        \"samples\": [\n          1963271422,\n          1752255578,\n          1965687736\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"happiness\",\n          \"sadness\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10353,\n        \"samples\": [\n          \"Bacon Fail at Gulb... lack of chef\",\n          \"just came back home from NE gig, another fun night skankin...what a workout!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"len\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 6,\n        \"max\": 161,\n        \"num_unique_values\": 149,\n        \"samples\": [\n          90,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "data.groupby('sentiment').describe()"
      ],
      "metadata": {
        "id": "1q3q4t6zlyTY",
        "outputId": "24eb41b8-5eeb-44d9-e209-83ab5ac43db3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          tweet_id                                                          \\\n",
              "             count          mean           std           min           25%   \n",
              "sentiment                                                                    \n",
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              "                                                        len             \\\n",
              "                    50%           75%           max   count       mean   \n",
              "sentiment                                                                \n",
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              "\n",
              "                                                     \n",
              "                 std  min   25%   50%    75%    max  \n",
              "sentiment                                            \n",
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            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "# Assuming 'content' column contains the text data\n",
        "text = ' '.join(data['content'].astype(str).tolist()) # Convert content to string\n",
        "\n",
        "# Generate wordcloud\n",
        "wordcloud = WordCloud(background_color = 'white', width = 1000, height = 1000).generate(text)\n",
        "\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.imshow(wordcloud)\n",
        "plt.title(\"WordCloud - Vocabulary from Reviews\", fontsize = 22)\n",
        "plt.axis(\"off\") # Remove axis\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ugcYGhlWpTPw",
        "outputId": "9f79e1b1-3ed2-439c-bbab-7cbd81218f1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Cleaning**"
      ],
      "metadata": {
        "id": "Kb2Yk6PYcSIu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXFXG8t_cbkV",
        "outputId": "a2fa6542-5f23-41ba-c486-4d6d68374eb5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Making all letters lowercase\n",
        "data['content'] = data['content'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n",
        "#Removing Punctuation, Symbols\n",
        "data['content'] = data['content'].str.replace('[^\\w\\s]',' ')\n",
        "#Removing Stop Words using NLTK\n",
        "from nltk.corpus import stopwords\n",
        "stop = stopwords.words('english')\n",
        "data['content'] = data['content'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop))"
      ],
      "metadata": {
        "id": "b36SIdCockAn"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xBz1Ff0cuLe",
        "outputId": "d3a352cf-9f1d-45d4-8e3d-dad8d5abc1d7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lemmatisation\n",
        "from textblob import Word\n",
        "data['content'] = data['content'].apply(lambda x: \" \".join([Word(word).lemmatize() for word in x.split()]))\n",
        "#Correcting Letter Repetitions\n",
        "import re\n",
        "def de_repeat(text):\n",
        "    pattern = re.compile(r\"(.)\\1{2,}\")\n",
        "    return pattern.sub(r\"\\1\\1\", text)\n",
        "#%%\n",
        "data['content'] = data['content'].apply(lambda x: \" \".join(de_repeat(x) for x in x.split()))"
      ],
      "metadata": {
        "id": "oBsiv33cc7h6"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next consideration is the idea that if a word is appearing only once in the entire sample of data, then it most likely has no influence in determining the sentiment of the text. Hence we can remove all the rarely occurring words from the dataset which are generally proper nouns and other insignificant words with respect to the current context."
      ],
      "metadata": {
        "id": "aDxaV5XddBMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Code to find the top 10,000 rarest words appearing in the data\n",
        "# Code to find the top 10,000 rarest words appearing in the data\n",
        "freq = pd.Series(' '.join(data['content']).split()).value_counts()[-10000:]\n",
        "# Removing all those rarely appearing words from the data\n",
        "freq = list(freq.index)\n",
        "data['content'] = data['content'].apply(lambda x: \" \".join(x for x in x.split() if x not in freq))"
      ],
      "metadata": {
        "id": "BeX0K4VqdLfQ"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PR-N7L7eY0eq",
        "outputId": "dc603587-5d0e-4a11-9ac2-d4651113109a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     tweet_id sentiment                                            content  \\\n",
            "1  1956967666   sadness                                     n bed headache   \n",
            "2  1956967696   sadness                                   funeral friday..   \n",
            "6  1956968487   sadness  sleep, im not! thinking old friend want. he's ...   \n",
            "8  1956969035   sadness                                         love. miss   \n",
            "9  1956969172   sadness                            i'm sorry least friday?   \n",
            "\n",
            "   len  \n",
            "1   60  \n",
            "2   35  \n",
            "6  132  \n",
            "8   39  \n",
            "9   42  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encode Label Categories**"
      ],
      "metadata": {
        "id": "uhOIQBDAYpbh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label encoding is important since most machine learning models cannot directly comprehend and analyze text labels or raw category data, we encode categories during the model-building process."
      ],
      "metadata": {
        "id": "zAuvzcKUZEZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Encode label for easy identification.\n",
        "data['sentiment'] = data['sentiment'].astype('category').cat.codes\n",
        "print(data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMW5yHkgZIHv",
        "outputId": "5bf6160f-63c5-4c59-8ed4-a7b138273d83"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     tweet_id  sentiment                                            content  \\\n",
            "1  1956967666          1                                     n bed headache   \n",
            "2  1956967696          1                                   funeral friday..   \n",
            "6  1956968487          1  sleep, im not! thinking old friend want. he's ...   \n",
            "8  1956969035          1                                         love. miss   \n",
            "9  1956969172          1                            i'm sorry least friday?   \n",
            "\n",
            "   len  \n",
            "1   60  \n",
            "2   35  \n",
            "6  132  \n",
            "8   39  \n",
            "9   42  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Feature Extraction**"
      ],
      "metadata": {
        "id": "ssV2gugzd8ks"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once you make the text data clean, precise, and error-free, each tweet is represented by a group of keywords. Now, we need to perform ‘Feature Extraction’, i.e extracting some parameters from the data that can be presented numerically. In this article, we consider two different features, TF-IDF & Count Vectors (Remember, we need numeric data for the math!). Split the data into training and testing parts before performing feature extraction."
      ],
      "metadata": {
        "id": "X_WPkA1NeIyc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Encoding output labels 'sadness' as '1' & 'happiness' as '0'\n",
        "from sklearn import preprocessing\n",
        "lbl_enc = preprocessing.LabelEncoder()\n",
        "y = lbl_enc.fit_transform(data.sentiment.values)\n",
        "# Splitting into training and testing data in 90:10 ratio\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(data.content.values, y, stratify=y, random_state=42, test_size=0.1, shuffle=True)"
      ],
      "metadata": {
        "id": "-TiBjwA5eND6"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Term Frequency-Inverse Document Frequency (TF-IDF): This parameter gives the relative importance of a term in the data and is a measure of how frequently and rarely it appears in the text. This can be directly extracted in python as follows-"
      ],
      "metadata": {
        "id": "gNnpO8bPelOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting TF-IDF parameters\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer(max_features=1000, analyzer='word',ngram_range=(1,3))\n",
        "X_train_tfidf = tfidf.fit_transform(X_train)\n",
        "X_val_tfidf = tfidf.fit_transform(X_val)"
      ],
      "metadata": {
        "id": "nJeoK5z2eptL"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Count Vectors: This is another feature we consider and as the name suggests we transform our tweet into an array having the count of appearances of each word in it. The intuition here is that the text that conveys similar emotions may have the same words repeated over and over again. This is more like the direct approach."
      ],
      "metadata": {
        "id": "5BKKXPy4etX1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting Count Vectors Parameters\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "count_vect = CountVectorizer(analyzer='word')\n",
        "count_vect.fit(data['content'])\n",
        "X_train_count =  count_vect.transform(X_train)\n",
        "X_val_count =  count_vect.transform(X_val)"
      ],
      "metadata": {
        "id": "Zh8mN1w5ezy-"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**4-Train our Model**</p>"
      ],
      "metadata": {
        "id": "wKtJSOAhe5aD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build some models using the TF-IDF features**"
      ],
      "metadata": {
        "id": "euxVvt6PfBj1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1-Multinomial Naive Bayes Classifier**"
      ],
      "metadata": {
        "id": "x0P93jlNfJal"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "# Model 1: Multinomial Naive Bayes Classifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "nb = MultinomialNB()\n",
        "nb.fit(X_train_tfidf, y_train)\n",
        "y_pred = nb.predict(X_val_tfidf)\n",
        "print('naive bayes tfidf accuracy %s' % accuracy_score(y_pred, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9lSl13yfN8r",
        "outputId": "70eaf96f-7017-4494-d4ff-35534d810af4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "naive bayes tfidf accuracy 0.5491329479768786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model 2: Linear SVM**"
      ],
      "metadata": {
        "id": "5GbnB79KffdK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "lsvm = SGDClassifier(alpha=0.001, random_state=5, max_iter=15, tol=None)\n",
        "lsvm.fit(X_train_tfidf, y_train)\n",
        "y_pred = lsvm.predict(X_val_tfidf)\n",
        "print('svm using tfidf accuracy %s' % accuracy_score(y_pred, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8Dk2pBAfkJs",
        "outputId": "d0f3895f-9190-4fc8-e4f3-3986fe80e698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "svm using tfidf accuracy 0.5616570327552987\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model 3: logistic regression**"
      ],
      "metadata": {
        "id": "ojmW6cmyfoWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 3: logistic regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression(C=1)\n",
        "logreg.fit(X_train_tfidf, y_train)\n",
        "y_pred = logreg.predict(X_val_tfidf)\n",
        "print('log reg tfidf accuracy %s' % accuracy_score(y_pred, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSIZmjUFfrj8",
        "outputId": "e77105b5-c056-4f51-f5c9-6d830d4e010b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "log reg tfidf accuracy 0.5568400770712909\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model 4: Random Forest Classifier**"
      ],
      "metadata": {
        "id": "oVXVXU6of0-s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(n_estimators=500)\n",
        "rf.fit(X_train_tfidf, y_train)\n",
        "y_pred = rf.predict(X_val_tfidf)\n",
        "print('random forest tfidf accuracy %s' % accuracy_score(y_pred, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c30fVkjBf6Ev",
        "outputId": "a501d01c-1dfb-4e5e-f649-e1ba97f6e7a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "random forest tfidf accuracy 0.5529865125240848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build models using count vectors features-**"
      ],
      "metadata": {
        "id": "shDMtAxUiB2P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 1: Multinomial Naive Bayes Classifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "nb = MultinomialNB()\n",
        "nb.fit(X_train_count, y_train)\n",
        "y_pred = nb.predict(X_val_count)\n",
        "print('naive bayes count vectors accuracy %s' % accuracy_score(y_pred, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ln5Bh10IiLRH",
        "outputId": "d8857586-d673-48bf-cc61-51af82f80dc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "naive bayes count vectors accuracy 0.7620423892100193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 2: Linear SVM\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "lsvm = SGDClassifier(alpha=0.001, random_state=5, max_iter=15, tol=None)\n",
        "lsvm.fit(X_train_count, y_train)\n",
        "y_pred = lsvm.predict(X_val_count)\n",
        "print('lsvm using count vectors accuracy %s' % accuracy_score(y_pred, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqLW2LMaiQvf",
        "outputId": "ab1422c6-1770-429a-c2bc-005ac39d77d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "lsvm using count vectors accuracy 0.7976878612716763\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 3: Logistic Regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression(C=1)\n",
        "logreg.fit(X_train_count, y_train)\n",
        "y_pred = logreg.predict(X_val_count)\n",
        "print('log reg count vectors accuracy %s' % accuracy_score(y_pred, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmKxOwHQiVrg",
        "outputId": "aab7d3b4-b5d3-4663-85c4-222e336dee12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "log reg count vectors accuracy 0.7928709055876686\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By using count vectors, we have a significant improvement in performance. The best model, linear SVM achieved up to 79.28% accuracy.\n",
        "\n",
        "This might be because of the nature of this specific dataset where the emotion of the text is heavily dependent on the presence of some significant adjectives.\n",
        "\n",
        "Let us now test how it performs in reality by giving this model some random text input."
      ],
      "metadata": {
        "id": "1SrQGFgRilnu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**5-Model Testing**</p>"
      ],
      "metadata": {
        "id": "b7iHNKtWirPN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Below are 8 random statements.\n",
        "#The first 4 depict happiness\n",
        "#The last 4 depict sadness\n",
        "tweets = pd.DataFrame(['I am very happy today! The atmosphere looks cheerful',\n",
        "'Things are looking great. It was such a good day',\n",
        "'Success is right around the corner. Lets celebrate this victory',\n",
        "'Everything is more beautiful when you experience them with a smile!',\n",
        "'Now this is my worst, okay? But I am gonna get better.',\n",
        "'I am tired, boss. Tired of being on the road, lonely as a sparrow in the rain. I am tired of all the pain I feel',\n",
        "'This is quite depressing. I am filled with sorrow',\n",
        "'His death broke my heart. It was a sad day'])\n",
        "# Doing some preprocessing on these tweets as done before\n",
        "tweets[0] = tweets[0].str.replace('[^\\w\\s]',' ')\n",
        "from nltk.corpus import stopwords\n",
        "stop = stopwords.words('english')\n",
        "tweets[0] = tweets[0].apply(lambda x: \" \".join(x for x in x.split() if x not in stop))\n",
        "from textblob import Word\n",
        "tweets[0] = tweets[0].apply(lambda x: \" \".join([Word(word).lemmatize() for word in x.split()]))\n",
        "# Extracting Count Vectors feature from our tweets\n",
        "tweet_count = count_vect.transform(tweets[0])\n",
        "#Predicting the emotion of the tweet using our already trained linear SVM\n",
        "tweet_pred = lsvm.predict(tweet_count)\n",
        "print(tweet_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZvpxqgOi5dT",
        "outputId": "8f07e1bb-1ced-452d-ad8f-fa896f932873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 1 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**6-Hugging Face Transformers**</p>"
      ],
      "metadata": {
        "id": "6oLhr7ktjVD9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hugging Face hosts a variety of transformer-based Language Models (LMs) specialized in addressing language understanding and language generation tasks, including but not limited to:\n",
        "\n",
        "1- Text classification\n",
        "\n",
        "2- Named Entity Recognition (NER)\n",
        "\n",
        "3- Text generation\n",
        "\n",
        "4- Question-answering\n",
        "\n",
        "5-Summarization\n",
        "\n",
        "6- Translation"
      ],
      "metadata": {
        "id": "uK41UXk_jq8Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Loading the Language Model**\n"
      ],
      "metadata": {
        "id": "HFHm1q7tkNOz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# ... (rest of your code)\n",
        "classifier = pipeline(\"text-classification\", model=\"j-hartmann/emotion-english-distilroberta-base\")\n",
        "\n",
        "# Step 1: Make predictions on the training and validation sets\n",
        "y_train_pred = [classifier(text)[0]['label'] for text in X_train]\n",
        "y_val_pred = [classifier(text)[0]['label'] for text in X_val]\n",
        "\n",
        "# Map string labels to numerical labels if needed\n",
        "label_mapping = {'anger': 0, 'disgust': 1, 'fear': 2, 'joy': 3, 'neutral': 4, 'sadness': 5, 'surprise': 6}\n",
        "y_train_pred_numeric = [label_mapping[label] for label in y_train_pred]\n",
        "y_val_pred_numeric = [label_mapping[label] for label in y_val_pred]\n",
        "\n",
        "# Step 2: Evaluate the performance\n",
        "print(\"Training Set Performance:\")\n",
        "print(classification_report(y_train, y_train_pred_numeric)) # Use numeric labels\n",
        "\n",
        "print(\"Validation Set Performance:\")\n",
        "print(classification_report(y_val, y_val_pred_numeric)) # Use numeric labels"
      ],
      "metadata": {
        "id": "qcfrF9x0wHdN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "02d929d7-12fc-4e1b-a8bb-1936ebee912a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.00k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "6acd23af5dba4fcabda68a192cd4b8db"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "pytorch_model.bin:   0%|          | 0.00/329M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/294 [00:00<?, ?B/s]"
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              "model_id": "e43be9dea9314b3db2ae0d63bce7ac4a"
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        },
        {
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e6f86b48c7c4af5940e10a6c9b29c67"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3b70eb88855477d8e2f5ea423b530d8"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set Performance:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.19      0.01      0.02      4688\n",
            "           1       0.86      0.01      0.03      4648\n",
            "           2       0.00      0.00      0.00         0\n",
            "           3       0.00      0.00      0.00         0\n",
            "           4       0.00      0.00      0.00         0\n",
            "           5       0.00      0.00      0.00         0\n",
            "           6       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.01      9336\n",
            "   macro avg       0.15      0.00      0.01      9336\n",
            "weighted avg       0.52      0.01      0.02      9336\n",
            "\n",
            "Validation Set Performance:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.19      0.01      0.02       521\n",
            "           1       1.00      0.01      0.03       517\n",
            "           2       0.00      0.00      0.00         0\n",
            "           3       0.00      0.00      0.00         0\n",
            "           4       0.00      0.00      0.00         0\n",
            "           5       0.00      0.00      0.00         0\n",
            "           6       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.01      1038\n",
            "   macro avg       0.17      0.00      0.01      1038\n",
            "weighted avg       0.60      0.01      0.02      1038\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pred"
      ],
      "metadata": {
        "id": "7CnEmoTh1ZRg",
        "outputId": "d987c4d3-c8c7-48c9-8ff1-40412c73ec9f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'sadness',\n",
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              " 'neutral',\n",
              " 'surprise',\n",
              " 'surprise',\n",
              " 'joy',\n",
              " 'neutral',\n",
              " 'neutral',\n",
              " 'joy',\n",
              " 'sadness',\n",
              " 'joy',\n",
              " 'sadness',\n",
              " 'sadness',\n",
              " 'sadness',\n",
              " 'joy',\n",
              " 'neutral',\n",
              " 'sadness',\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#example_tweet = \"I love hugging face transformers!\"\n",
        "prediction = classifier(y_train_pred)\n",
        "print(prediction)"
      ],
      "metadata": {
        "id": "JR8Mh2yt1LG2",
        "outputId": "858cc4c8-e57b-407b-e757-b196ba677f1a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'fear', 'score': 0.9867658615112305}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'fear', 'score': 0.9867658615112305}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'anger', 'score': 0.5015983581542969}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'disgust', 'score': 0.9804266691207886}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'surprise', 'score': 0.7743749022483826}, {'label': 'neutral', 'score': 0.62220299243927}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'joy', 'score': 0.9535831212997437}, {'label': 'sadness', 'score': 0.9736422896385193}, {'label': 'neutral', 'score': 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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_val_pred"
      ],
      "metadata": {
        "id": "3_LqrHZg0I-y",
        "outputId": "5d2b9975-7c56-4104-d1f2-62bca6122341",
        "colab": {
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        }
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**7-DistilBERT**</p>"
      ],
      "metadata": {
        "id": "AXNstkzWkQDI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import all Necessary Libraries**\n",
        "\n",
        "Let import the necessary libraries needed for this project."
      ],
      "metadata": {
        "id": "jOxPZ5e7lOa6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertTokenizer\n",
        "from transformers import TFDistilBertForSequenceClassification\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "cG4rPQS5lT65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Preparation**\n"
      ],
      "metadata": {
        "id": "Bmi51QtGlj_K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "At this point, we need to start building our model for categorizing the label columns."
      ],
      "metadata": {
        "id": "3A4drdETlo3w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Split DataFrame into Feature and Label**\n"
      ],
      "metadata": {
        "id": "Ag-QS4cnltFA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Encoding output labels 'sadness' as '1' & 'happiness' as '0'\n",
        "from sklearn import preprocessing\n",
        "lbl_enc = preprocessing.LabelEncoder()\n",
        "y = lbl_enc.fit_transform(data.sentiment.values)\n",
        "# Splitting into training and testing data in 90:10 ratio\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(data.content.values, y, stratify=y, random_state=42, test_size=0.1, shuffle=True)\n",
        "\n",
        "# Convert X_train and X_val to lists of strings\n",
        "X_train = X_train.astype(str).tolist()  # Ensure each element is a string\n",
        "X_val = X_val.astype(str).tolist()    # Ensure each element is a string\n"
      ],
      "metadata": {
        "id": "8tgT11M6l7QA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Download the Model needed from the Transformers Library**\n",
        "\n",
        "Initializes a tokenizer for the DistilBERT model using the DistilBertTokenizer class from the Hugging Face transformers library."
      ],
      "metadata": {
        "id": "UYvAdi4OmIC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertTokenizer\n",
        "from transformers import TFDistilBertForSequenceClassification\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "train_encodings = tokenizer(X_train, truncation=True, padding=True) # Pass the list of strings\n",
        "val_encodings = tokenizer(X_val, truncation=True, padding=True)   # Pass the list of strings"
      ],
      "metadata": {
        "id": "MnEzuWAYmOKb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create TensorFlow Dataset**\n",
        "\n",
        "We will need to create a TensorFlow Dataset object from tensors using the tf.data.Dataset.from_tensor_slices() method. This will be done for both train and validate dataset."
      ],
      "metadata": {
        "id": "WJ4SQkvsmUwY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    y_train  # Use y_train instead of train_labels\n",
        "))\n",
        "val_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(val_encodings),\n",
        "    y_val   # Use y_val instead of val_labels\n",
        "))"
      ],
      "metadata": {
        "id": "60aM2KEumbHR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TFTrainer Class for Fine-tuning**\n",
        "\n",
        "The code below uses TensorFlow to initialize and compile a sequence classification model based on the DistilBERT architecture."
      ],
      "metadata": {
        "id": "UJIX5Wnkmgnk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=5)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5, epsilon=1e-08)\n",
        "model.compile(optimizer=optimizer, loss=model.hf_compute_loss, metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "bg5KdEJWmmVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train Model Using TensorFlow**\n",
        "\n",
        "Let’s train the model in TensorFlow with early stopping, we also set the number of iterations/epochs to 2. For better accuracy, we might need to increase the number of iterations and parameter tuning.\n",
        "\n",
        "In machine learning, early stopping is a technique used to improve a model’s generalization and performance during training by preventing overfitting."
      ],
      "metadata": {
        "id": "yK3f1yI0muwP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
        "\n",
        "model.fit(train_dataset.shuffle(1000).batch(16),\n",
        "epochs=2,\n",
        "batch_size=16,\n",
        "validation_data=val_dataset.shuffle(1000).batch(16),\n",
        "callbacks=[early_stopping])\n"
      ],
      "metadata": {
        "id": "44-6Oha0m12y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Architecture Summary**\n",
        "\n",
        "The model.summary() is used to display a neural network model architecture summary."
      ],
      "metadata": {
        "id": "-f4UhxFem71Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " model.summary()\n"
      ],
      "metadata": {
        "id": "JTZCKxqnnBGn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Save Model in Local Directory**\n",
        "\n",
        "For reusability purposes, let can save the model using the following line of code. This will create an h5 file in any desired location of your choice."
      ],
      "metadata": {
        "id": "u4lnjgSznFjm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "save_directory = \"Multitext_Classification_colab\" # Change this to your preferred location\n",
        "\n",
        "model.save_pretrained(save_directory)\n",
        "tokenizer.save_pretrained(save_directory)"
      ],
      "metadata": {
        "id": "KB6WbNginONX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load Save Model**\n",
        "\n",
        "We can recall the saved model for future use by using the DistilBertTokenizer library. The line of code below helps load the .h5 file in the saved directory and be reused for future used cases."
      ],
      "metadata": {
        "id": "uULp-8awnTLn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Change the save_directory to the actual location where the model was saved.\n",
        "# If it was saved to \"Multitext_Classification_colab\", use that instead:\n",
        "save_directory = \"Multitext_Classification_colab\"\n",
        "\n",
        "loaded_tokenizer = DistilBertTokenizer.from_pretrained(save_directory)\n",
        "loaded_model = TFDistilBertForSequenceClassification.from_pretrained(save_directory)"
      ],
      "metadata": {
        "id": "MP91aGT_naBi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluate Model**\n",
        "\n",
        "Now that we are done with the training of the model, we need to evaluate it using actual data and see how best it performs."
      ],
      "metadata": {
        "id": "EpD3Eio_ngXo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "\n",
        "# Assuming 'data' is your DataFrame and 'sentiment' column contains labels\n",
        "test_labels = data['sentiment'].values\n",
        "\n",
        "# Define test_data - You need to prepare your test data for prediction.\n",
        "# This could involve tokenizing it using the loaded_tokenizer.\n",
        "# Replace '...' with the appropriate code for preparing your test data\n",
        "test_data = loaded_tokenizer(list(data['content']), padding=True, truncation=True, return_tensors=\"tf\")\n",
        "\n",
        "# Get predictions from the loaded model\n",
        "y_pred_probs = loaded_model.predict(test_data)\n",
        "# Assuming your model outputs probabilities, you might need to get the predicted labels:\n",
        "y_pred_numerical = np.argmax(y_pred_probs.logits, axis=1) # Extract predicted labels\n",
        "\n",
        "# Create a mapping between numerical labels and sentiment labels\n",
        "# Assuming your sentiment labels are: ['happiness', 'sadness']\n",
        "label_mapping = {0: 'happiness', 1: 'sadness'}\n",
        "\n",
        "# Convert numerical predictions to sentiment labels\n",
        "y_pred_labels = [label_mapping[pred] for pred in y_pred_numerical]\n",
        "\n",
        "# Now you can use test_labels in the classification_report\n",
        "print(classification_report(test_labels, y_pred_labels))"
      ],
      "metadata": {
        "id": "u8oCRgp4nnOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix**\n",
        "\n",
        "In machine learning, a confusion matrix is a tool used to assess how well categorization algorithms perform. This is due to the fact it offers a more detailed picture of a model’s accuracy than just a simple overall percentage.\n",
        "\n",
        "Let’s start by creating a function for making predictions on text data using a loaded tokenizer and a loaded model."
      ],
      "metadata": {
        "id": "6Y6-I2tJnt_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming y_pred_labels from ipython-input-33-d34652aacf8a contains the sentiment labels (strings)\n",
        "# Replace 'y_pred' with 'y_pred_labels' in the confusion_matrix function\n",
        "confusion = confusion_matrix(test_labels, y_pred_labels)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.set(font_scale=1.2)\n",
        "sns.heatmap(confusion, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False, square=True,\n",
        "            xticklabels=[\"Business\", \"Entertainment\", \"Politics\", \"Sport\", \"Tech\"],\n",
        "            yticklabels=[\"Business\", \"Entertainment\", \"Politics\", \"Sport\", \"Tech\"])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "jZKFzn8qn1CH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **References**"
      ],
      "metadata": {
        "id": "a8xm7HCWeLf7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1-Using Hugging Face Transformers for Emotion Detection in Text](https://www.kdnuggets.com/using-hugging-face-transformers-for-emotion-detection-in-text?fbclid=IwY2xjawExY0BleHRuA2FlbQIxMAABHZ3RbcIRCZTEwZn3IQ1nd-4dHcP9OgD6dSGZ8sP9u4B4C5CSU4Gep7JNig_aem_ckeIZhX121O4XrywLl_V1A)\n",
        "\n",
        "[2-How to Handle Large Text Inputs with Longformer and Hugging Face Transformers](https://www.kdnuggets.com/how-to-handle-large-text-inputs-with-longformer-and-hugging-face-transformers)\n",
        "\n",
        "[3-DistilBERT for Multiclass Text Classification Using Transformers](https://medium.com/@kiddojazz/distilbert-for-multiclass-text-classification-using-transformers-d6374e6678ba)\n",
        "\n",
        "[4-Twitter-Sentiment-Analysis](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/tree/master)"
      ],
      "metadata": {
        "id": "zxrRKSuseTUc"
      }
    }
  ]
}