Modelado_de_Tópicos_Tesla.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Conectar drive personal"
      ],
      "metadata": {
        "id": "7SfLZ9CQrNge"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "beepebp8cgDc"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importar librerías necesarias e instalar modelo para modelado de tópicos BERTopic"
      ],
      "metadata": {
        "id": "0UKbIFRFrUL6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "!pip install bertopic"
      ],
      "metadata": {
        "id": "xGmXrd3kcj4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importar modelo BERTopic e instanciarlo con parámetros de configuración básicos: lenguaje, cálculo de probabilidad de los tópicos obtenidos por documento y seguimiento de las fases del modelo (verbose)"
      ],
      "metadata": {
        "id": "6ExAFd36rW_4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#IMPORTAR MODELO\n",
        "from bertopic import BERTopic\n",
        "topic_model = BERTopic(language=\"english\", calculate_probabilities=False, verbose=True)"
      ],
      "metadata": {
        "id": "1UhpuOOPconq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primero se lee el dataset con los tweets limpios y pre-procesados. A continuación, se transforma la columna 'body' con los tweets a una lista de strings para utilizarla como entrada al modelo BERTopic. Finalmente, se obtienen los tópicos más relevantes del tópico"
      ],
      "metadata": {
        "id": "Op8w2Mv6raYJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#leer dataset\n",
        "data_tesla = pd.read_csv('/content/drive/MyDrive/TFG ADE/Data_Tesla_Limpio.csv')\n",
        "data=(data_tesla['body'].astype(str)).tolist()\n",
        "topics, _ = topic_model.fit_transform(data)"
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/5988 [00:00<?, ?it/s]"
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          "text": [
            "2023-11-14 17:43:30,801 - BERTopic - Transformed documents to Embeddings\n",
            "2023-11-14 17:51:43,932 - BERTopic - Reduced dimensionality\n",
            "2023-11-14 17:52:03,969 - BERTopic - Clustered reduced embeddings\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se obtiene información general sobre los tópicos obtenidos por el modelo: nombre/identificador, términos representativos y documentos representativos (-1 es el tópico outlier, no se tiene en cuenta en el análisis)"
      ],
      "metadata": {
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      "source": [
        "freq = topic_model.get_topic_info()\n",
        "freq"
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      },
      "execution_count": null,
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            "text/plain": [
              "      Topic  Count                                               Name  \\\n",
              "0        -1  85381                      -1_autopilot_musk_car_battery   \n",
              "1         0   2116                        0_solar_roof_solarcity_tile   \n",
              "2         1    992                              1_nan_nano_ching_yeah   \n",
              "3         2    980                          2_gspc_surge_fall_outlook   \n",
              "4         3    884                       3_elon_elons_stuttering_tapp   \n",
              "...     ...    ...                                                ...   \n",
              "2122   2121     10             2121_signal_issued_potnetial_outpretty   \n",
              "2123   2122     10               2122_burger_sandwich_restocked_pbhto   \n",
              "2124   2123     10  2123_localnational_fortuitous_extensionbased_b...   \n",
              "2125   2124     10                     2124_4200_yuasa_jvor_whyteslas   \n",
              "2126   2125     10             2125_expiry_expiring_11sold_sweepsjuly   \n",
              "\n",
              "                                         Representation  \\\n",
              "0     [autopilot, musk, car, battery, elonmusk, sale...   \n",
              "1     [solar, roof, solarcity, tile, city, panel, ba...   \n",
              "2                  [nan, nano, ching, yeah, , , , , , ]   \n",
              "3     [gspc, surge, fall, outlook, nflx, soar, rise,...   \n",
              "4     [elon, elons, stuttering, tapp, lie, wright, g...   \n",
              "...                                                 ...   \n",
              "2122  [signal, issued, potnetial, outpretty, abux, n...   \n",
              "2123  [burger, sandwich, restocked, pbhto, bintsla, ...   \n",
              "2124  [localnational, fortuitous, extensionbased, bu...   \n",
              "2125  [4200, yuasa, jvor, whyteslas, bydgf, jvaesc, ...   \n",
              "2126  [expiry, expiring, 11sold, sweepsjuly, 345, 35...   \n",
              "\n",
              "                                    Representative_Docs  \n",
              "0     [stock value isnt useful whats interesting fla...  \n",
              "1     [solar panel roof, car battery solar roof, sol...  \n",
              "2                                       [nan, nan, nan]  \n",
              "3     [gspc nflx share rise earnings beat, gspc nflx...  \n",
              "4                                    [elon, elon, elon]  \n",
              "...                                                 ...  \n",
              "2122  [weekly signal issued monday amfor solid move ...  \n",
              "2123      [go month bynd burger, coming burger, burger]  \n",
              "2124  [anyone show sale increased localnational gove...  \n",
              "2125  [go time high believe achieve 4200 share year ...  \n",
              "2126  [call sweepsjuly 19th expiry, 11sold call area...  \n",
              "\n",
              "[2127 rows x 5 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
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              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>85381</td>\n",
              "      <td>-1_autopilot_musk_car_battery</td>\n",
              "      <td>[autopilot, musk, car, battery, elonmusk, sale...</td>\n",
              "      <td>[stock value isnt useful whats interesting fla...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2116</td>\n",
              "      <td>0_solar_roof_solarcity_tile</td>\n",
              "      <td>[solar, roof, solarcity, tile, city, panel, ba...</td>\n",
              "      <td>[solar panel roof, car battery solar roof, sol...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>980</td>\n",
              "      <td>2_gspc_surge_fall_outlook</td>\n",
              "      <td>[gspc, surge, fall, outlook, nflx, soar, rise,...</td>\n",
              "      <td>[gspc nflx share rise earnings beat, gspc nflx...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>884</td>\n",
              "      <td>3_elon_elons_stuttering_tapp</td>\n",
              "      <td>[elon, elons, stuttering, tapp, lie, wright, g...</td>\n",
              "      <td>[elon, elon, elon]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2122</th>\n",
              "      <td>2121</td>\n",
              "      <td>10</td>\n",
              "      <td>2121_signal_issued_potnetial_outpretty</td>\n",
              "      <td>[signal, issued, potnetial, outpretty, abux, n...</td>\n",
              "      <td>[weekly signal issued monday amfor solid move ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2123</th>\n",
              "      <td>2122</td>\n",
              "      <td>10</td>\n",
              "      <td>2122_burger_sandwich_restocked_pbhto</td>\n",
              "      <td>[burger, sandwich, restocked, pbhto, bintsla, ...</td>\n",
              "      <td>[go month bynd burger, coming burger, burger]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2124</th>\n",
              "      <td>2123</td>\n",
              "      <td>10</td>\n",
              "      <td>2123_localnational_fortuitous_extensionbased_b...</td>\n",
              "      <td>[localnational, fortuitous, extensionbased, bu...</td>\n",
              "      <td>[anyone show sale increased localnational gove...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2125</th>\n",
              "      <td>2124</td>\n",
              "      <td>10</td>\n",
              "      <td>2124_4200_yuasa_jvor_whyteslas</td>\n",
              "      <td>[4200, yuasa, jvor, whyteslas, bydgf, jvaesc, ...</td>\n",
              "      <td>[go time high believe achieve 4200 share year ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2126</th>\n",
              "      <td>2125</td>\n",
              "      <td>10</td>\n",
              "      <td>2125_expiry_expiring_11sold_sweepsjuly</td>\n",
              "      <td>[expiry, expiring, 11sold, sweepsjuly, 345, 35...</td>\n",
              "      <td>[call sweepsjuly 19th expiry, 11sold call area...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ya que se obtienen un gran número de documentos pertenecientes al tópico -1 (outlier), se reducen los mismos y se actualizan las representaciones de los tópicos obtenidos"
      ],
      "metadata": {
        "id": "PFK9vML_rjSz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_topics = topic_model.reduce_outliers(data, topics)\n",
        "topic_model.update_topics(data, topics=new_topics)"
      ],
      "metadata": {
        "id": "f1HcZ6F09vuk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se obtiene la información general sin tantos outliers"
      ],
      "metadata": {
        "id": "tpLMGBaUryo9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "freq = topic_model.get_topic_info()\n",
        "freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "RKAtSxIz-GMj",
        "outputId": "f441fd95-6dcd-4a00-c98d-cef8e62181ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Topic  Count                                          Name  \\\n",
              "0        -1    192    -1_oniontsla_tslaentry_illegaltsla_caching   \n",
              "1         0   2180                   0_solar_roof_solarcity_tile   \n",
              "2         1    992                         1_nan_nano_ching_yeah   \n",
              "3         2   1003                     2_gspc_surge_fall_outlook   \n",
              "4         3    905                  3_elons_elon_stuttering_tapp   \n",
              "...     ...    ...                                           ...   \n",
              "2122   2121     18  2121_issued_signal_yesterdaystock_alreadyspy   \n",
              "2123   2122     31          2122_burger_sandwich_ribt_vegitarian   \n",
              "2124   2123     54   2123_sticker_restriction_extended_household   \n",
              "2125   2124     18                2124_4200_dealt_obsolete_yuasa   \n",
              "2126   2125     54                  2125_expiry_magnet_240c_2425   \n",
              "\n",
              "                                         Representation  \\\n",
              "0     [oniontsla, tslaentry, illegaltsla, caching, t...   \n",
              "1     [solar, roof, solarcity, tile, city, panel, ba...   \n",
              "2                  [nan, nano, ching, yeah, , , , , , ]   \n",
              "3     [gspc, surge, fall, outlook, nflx, soar, rise,...   \n",
              "4     [elons, elon, stuttering, tapp, lie, wright, g...   \n",
              "...                                                 ...   \n",
              "2122  [issued, signal, yesterdaystock, alreadyspy, t...   \n",
              "2123  [burger, sandwich, ribt, vegitarian, mash, che...   \n",
              "2124  [sticker, restriction, extended, household, on...   \n",
              "2125  [4200, dealt, obsolete, yuasa, mitsubishigs, w...   \n",
              "2126  [expiry, magnet, 240c, 2425, stepping, 19th, 3...   \n",
              "\n",
              "                                    Representative_Docs  \n",
              "0     [stock value isnt useful whats interesting fla...  \n",
              "1     [solar panel roof, car battery solar roof, sol...  \n",
              "2                                       [nan, nan, nan]  \n",
              "3     [gspc nflx share rise earnings beat, gspc nflx...  \n",
              "4                                    [elon, elon, elon]  \n",
              "...                                                 ...  \n",
              "2122  [weekly signal issued monday amfor solid move ...  \n",
              "2123      [go month bynd burger, coming burger, burger]  \n",
              "2124  [anyone show sale increased localnational gove...  \n",
              "2125  [go time high believe achieve 4200 share year ...  \n",
              "2126  [call sweepsjuly 19th expiry, 11sold call area...  \n",
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              "[2127 rows x 5 columns]"
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              "      <td>2121</td>\n",
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              "      <td>2121_issued_signal_yesterdaystock_alreadyspy</td>\n",
              "      <td>[issued, signal, yesterdaystock, alreadyspy, t...</td>\n",
              "      <td>[weekly signal issued monday amfor solid move ...</td>\n",
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              "      <th>2123</th>\n",
              "      <td>2122</td>\n",
              "      <td>31</td>\n",
              "      <td>2122_burger_sandwich_ribt_vegitarian</td>\n",
              "      <td>[burger, sandwich, ribt, vegitarian, mash, che...</td>\n",
              "      <td>[go month bynd burger, coming burger, burger]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2124</th>\n",
              "      <td>2123</td>\n",
              "      <td>54</td>\n",
              "      <td>2123_sticker_restriction_extended_household</td>\n",
              "      <td>[sticker, restriction, extended, household, on...</td>\n",
              "      <td>[anyone show sale increased localnational gove...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2125</th>\n",
              "      <td>2124</td>\n",
              "      <td>18</td>\n",
              "      <td>2124_4200_dealt_obsolete_yuasa</td>\n",
              "      <td>[4200, dealt, obsolete, yuasa, mitsubishigs, w...</td>\n",
              "      <td>[go time high believe achieve 4200 share year ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2126</th>\n",
              "      <td>2125</td>\n",
              "      <td>54</td>\n",
              "      <td>2125_expiry_magnet_240c_2425</td>\n",
              "      <td>[expiry, magnet, 240c, 2425, stepping, 19th, 3...</td>\n",
              "      <td>[call sweepsjuly 19th expiry, 11sold call area...</td>\n",
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              "      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-44163f68-2a08-4039-af73-8449e9de4353 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-44163f68-2a08-4039-af73-8449e9de4353');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f0da8447-950c-458f-8dc5-23ce263aa492')\"\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",
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              "    <g>\n",
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              "\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",
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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",
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              "  @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",
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              "      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-f0da8447-950c-458f-8dc5-23ce263aa492 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se extraen los n términos top del tópico más relevante/repetido (0) y la puntuación c-TF-IDF de cada uno"
      ],
      "metadata": {
        "id": "JuQ96Xdhr2Nx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.get_topic(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29J4LiRTc2PR",
        "outputId": "def26816-9ce3-4c8b-9253-c99ce0705093"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('solar', 0.01760027467952953),\n",
              " ('roof', 0.017043836447936053),\n",
              " ('solarcity', 0.01638360065103524),\n",
              " ('tile', 0.010647335229350924),\n",
              " ('city', 0.008134965581446704),\n",
              " ('panel', 0.007029349518304793),\n",
              " ('bailout', 0.005115056075276034),\n",
              " ('installation', 0.004787304330391724),\n",
              " ('shingle', 0.004786569358671893),\n",
              " ('acquisition', 0.003870052534784978)]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Debido al elevado número de tópicos encontrado (2127) y la limitación computacional, no es posible representar la distancia intertópica inicial"
      ],
      "metadata": {
        "id": "T5NxUFYGr5kE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DISTANCIA INTERTOPICA INICIAL\n",
        "topic_model.visualize_topics()"
      ],
      "metadata": {
        "id": "LsF9zNF9upKK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se guarda el modelo para no perder la clasificación inicial cuando expire la sesión"
      ],
      "metadata": {
        "id": "H5WuuKjXsIa8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.save('/content/drive/MyDrive/TFG ADE/teslatopicinicial')"
      ],
      "metadata": {
        "id": "jribK_usup3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se agrupan los tópicos para obtener las relaciones existentes entre ellos y se visualiza la jerarquía de los 25 más relevantes, con el objetivo de reducir el número de tópicos"
      ],
      "metadata": {
        "id": "FOqflKH6sLay"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#JERARQUIA INICIAL QUE AYUDA A REDUCIR EL NUMERO DE TOPICOS\n",
        "topic_model.visualize_hierarchy(top_n_topics=25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "id": "q1gzEOA73I8_",
        "outputId": "01849088-a2d3-4345-afc4-3337b636971f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Se vuelve a cargar el modelo"
      ],
      "metadata": {
        "id": "9pCuGYtCsOTa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo = BERTopic.load('/content/drive/MyDrive/TFG ADE/teslatopicinicial')"
      ],
      "metadata": {
        "id": "9FV91SqwAypV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se van reduciendo el número de tópicos y visualizando la distancia intertópica hasta obtener el número óptimo (20) en el que la distancia es lo suficientemente grande como para que estos sean diferenciables"
      ],
      "metadata": {
        "id": "4M3eHci3sa_O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se van reduciendo el número de tópicos y visualizando la distancia intertópica hasta obtener el número óptimo (22) en el que la distancia es lo suficientemente grande como para que estos sean diferenciables"
      ],
      "metadata": {
        "id": "YWBIVwV0w2om"
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    },
    {
      "cell_type": "code",
      "source": [
        "#PRUEBAS TOPIC REDUCTION Y DISTANCIA INTERTOPICA RESULTANTE\n",
        "modelo.reduce_topics(data, nr_topics=20)\n",
        "modelo.visualize_topics()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "4pbtZXuV4hBG",
        "outputId": "09a61e52-f953-4e72-fa2e-ae861c97efa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-11-14 18:09:16,245 - BERTopic - Reduced number of topics from 22 to 20\n"
          ]
        },
        {
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "De nuevo se obtiene la información general sobre los 20 tópicos resultantes. Se observa que el 1, 17 y 18 corresponden a acumulación de tweets \"basura\", por lo que no se tendrán en cuenta para el posterior análisis"
      ],
      "metadata": {
        "id": "uzzQusdHsneM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.get_topic_info()"
      ],
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          "height": 1602
        },
        "id": "qqfiXCkc5Fx6",
        "outputId": "8e5a5be6-962b-4b9f-e8bf-a2b2c2ac9e20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Topic   Count                                             Name  \\\n",
              "0      -1     192       -1_oniontsla_tslaentry_illegaltsla_caching   \n",
              "1       0  187446                         0_trade_stock_nflx_model   \n",
              "2       1     992                            1_nan_nano_ching_yeah   \n",
              "3       2     772                2_cnbc_cramer_cnbcfastmoney_stock   \n",
              "4       3     627            3_bagholderquotes_tent_baggie_gifford   \n",
              "5       4     281               4_lidar_vaporware_rendering_render   \n",
              "6       5     200                          5_lotto_call_put_friday   \n",
              "7       6     133            6_q32019_q3earnings_qtrresearch_model   \n",
              "8       7     110                          7_vwap_bwaa_bwaaa_break   \n",
              "9       8     110               8_advice_investment_long_providing   \n",
              "10      9     107       9_whistleblower_employee_retaliation_fraud   \n",
              "11     10     106              10_anxiety_sociopath_range_disorder   \n",
              "12     11      92           11_consolidation_resistance_break_move   \n",
              "13     12      92              12_allelectric_porsche_model_taycan   \n",
              "14     13      79           13_timseymour_short_cnbc_cnbcfastmoney   \n",
              "15     14      68        14_cheer_cheerleader_squeezy_cheerleading   \n",
              "16     15      67                       15_wash_rinse_washing_leak   \n",
              "17     16      47  16_virtue_signaling_signalling_virtuesignalling   \n",
              "18     17      45    17_9999999999_disclaimerpremarket_1000_100000   \n",
              "19     18      32                      18_let_goooo_goooooo_gooooo   \n",
              "\n",
              "                                       Representation  \\\n",
              "0   [oniontsla, tslaentry, illegaltsla, caching, t...   \n",
              "1   [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "2                [nan, nano, ching, yeah, , , , , , ]   \n",
              "3   [cnbc, cramer, cnbcfastmoney, stock, believer,...   \n",
              "4   [bagholderquotes, tent, baggie, gifford, bagho...   \n",
              "5   [lidar, vaporware, rendering, render, product,...   \n",
              "6   [lotto, call, put, friday, play, ticket, bough...   \n",
              "7   [q32019, q3earnings, qtrresearch, model, produ...   \n",
              "8   [vwap, bwaa, bwaaa, break, entry, short, long,...   \n",
              "9   [advice, investment, long, providing, give, sh...   \n",
              "10  [whistleblower, employee, retaliation, fraud, ...   \n",
              "11  [anxiety, sociopath, range, disorder, psychopa...   \n",
              "12  [consolidation, resistance, break, move, trend...   \n",
              "13  [allelectric, porsche, model, taycan, truck, p...   \n",
              "14  [timseymour, short, cnbc, cnbcfastmoney, posit...   \n",
              "15  [cheer, cheerleader, squeezy, cheerleading, oc...   \n",
              "16  [wash, rinse, washing, leak, water, repeat, ca...   \n",
              "17  [virtue, signaling, signalling, virtuesignalli...   \n",
              "18  [9999999999, disclaimerpremarket, 1000, 100000...   \n",
              "19  [let, goooo, goooooo, gooooo, gooo, gooooooooo...   \n",
              "\n",
              "                                  Representative_Docs  \n",
              "0                   [oniontsla, oniontsla, oniontsla]  \n",
              "1   [dont miss next free option trade sign daily f...  \n",
              "2                                     [nan, nan, nan]  \n",
              "3                     [problem cnbc, cnbc time, cnbc]  \n",
              "4   [built tent, bagholderquotes, good bagholderqu...  \n",
              "5   [committed lidar useless dumping lidar cheap s...  \n",
              "6                         [lotto lotto, lotto, lotto]  \n",
              "7   [opportunity increase demand q3earnings q32019...  \n",
              "8   [covered vwap held vwap bounce, vwap call, vwa...  \n",
              "9      [free advice worth, give advice, great advice]  \n",
              "10  [year later whistleblower ignored, national wh...  \n",
              "11  [range anxiety affect driver driver, soon reme...  \n",
              "12  [consolidation higher, break consolidation, mo...  \n",
              "13  [ford show allelectric f150 prototype, capture...  \n",
              "14  [timseymour short cover, good timseymour, tims...  \n",
              "15  [input cheer, higher first cheer, picked share...  \n",
              "16  [rinse wash repeat, someone forgot tell tslas ...  \n",
              "17  [virtue signaling sucka, virtue signalling cro...  \n",
              "18  [1000 tell 1000, 1000, quality 19999999999 999...  \n",
              "19                  [let goooo, let goooo, let goooo]  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
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              "      <td>0_trade_stock_nflx_model</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2_cnbc_cramer_cnbcfastmoney_stock</td>\n",
              "      <td>[cnbc, cramer, cnbcfastmoney, stock, believer,...</td>\n",
              "      <td>[problem cnbc, cnbc time, cnbc]</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>627</td>\n",
              "      <td>3_bagholderquotes_tent_baggie_gifford</td>\n",
              "      <td>[bagholderquotes, tent, baggie, gifford, bagho...</td>\n",
              "      <td>[built tent, bagholderquotes, good bagholderqu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>281</td>\n",
              "      <td>4_lidar_vaporware_rendering_render</td>\n",
              "      <td>[lidar, vaporware, rendering, render, product,...</td>\n",
              "      <td>[committed lidar useless dumping lidar cheap s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>200</td>\n",
              "      <td>5_lotto_call_put_friday</td>\n",
              "      <td>[lotto, call, put, friday, play, ticket, bough...</td>\n",
              "      <td>[lotto lotto, lotto, lotto]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>133</td>\n",
              "      <td>6_q32019_q3earnings_qtrresearch_model</td>\n",
              "      <td>[q32019, q3earnings, qtrresearch, model, produ...</td>\n",
              "      <td>[opportunity increase demand q3earnings q32019...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>110</td>\n",
              "      <td>7_vwap_bwaa_bwaaa_break</td>\n",
              "      <td>[vwap, bwaa, bwaaa, break, entry, short, long,...</td>\n",
              "      <td>[covered vwap held vwap bounce, vwap call, vwa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>110</td>\n",
              "      <td>8_advice_investment_long_providing</td>\n",
              "      <td>[advice, investment, long, providing, give, sh...</td>\n",
              "      <td>[free advice worth, give advice, great advice]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>107</td>\n",
              "      <td>9_whistleblower_employee_retaliation_fraud</td>\n",
              "      <td>[whistleblower, employee, retaliation, fraud, ...</td>\n",
              "      <td>[year later whistleblower ignored, national wh...</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>106</td>\n",
              "      <td>10_anxiety_sociopath_range_disorder</td>\n",
              "      <td>[anxiety, sociopath, range, disorder, psychopa...</td>\n",
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              "      <th>12</th>\n",
              "      <td>11</td>\n",
              "      <td>92</td>\n",
              "      <td>11_consolidation_resistance_break_move</td>\n",
              "      <td>[consolidation, resistance, break, move, trend...</td>\n",
              "      <td>[consolidation higher, break consolidation, mo...</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>92</td>\n",
              "      <td>12_allelectric_porsche_model_taycan</td>\n",
              "      <td>[allelectric, porsche, model, taycan, truck, p...</td>\n",
              "      <td>[ford show allelectric f150 prototype, capture...</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>79</td>\n",
              "      <td>13_timseymour_short_cnbc_cnbcfastmoney</td>\n",
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              "      <th>15</th>\n",
              "      <td>14</td>\n",
              "      <td>68</td>\n",
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              "      <th>16</th>\n",
              "      <td>15</td>\n",
              "      <td>67</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16</td>\n",
              "      <td>47</td>\n",
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              "      <td>[virtue signaling sucka, virtue signalling cro...</td>\n",
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              "      <td>17</td>\n",
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              "      <td>[1000 tell 1000, 1000, quality 19999999999 999...</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>18</td>\n",
              "      <td>32</td>\n",
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              "      <td>[let goooo, let goooo, let goooo]</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se guarda el modelo con los 20 tópicos resultantes para no perderlo cuando expire la sesión"
      ],
      "metadata": {
        "id": "j4KNru3NsrUb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.save('/content/drive/MyDrive/TFG ADE/teslatopic20')"
      ],
      "metadata": {
        "id": "2r_jY3T-B_9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se obtene la información sobre los documentos que sirvieron como base para la categorización de gtópicos, incluyendo los documentos en sí, sus respectivos temas, el nombre de cada tema, las n palabras principales de cada tema, si es un documento representativo y la probabilidad de agrupación si se trata del modelo de agrupación"
      ],
      "metadata": {
        "id": "opCGKWGPsyAS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.get_document_info(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fdixhX19JK3m",
        "outputId": "731f529b-8038-48f4-90e7-16b7d51e0c05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Document  Topic  \\\n",
              "0       patent portfolio company combined self driving...      0   \n",
              "1       joint development driving system high automati...      0   \n",
              "2       suck never heard dealer able least find equiva...      0   \n",
              "3       elonmusk autopilot almost another twice servic...      0   \n",
              "4                                             paid spread      0   \n",
              "...                                                   ...    ...   \n",
              "191593  getting wasted thanks nhtsagov approved autopi...      0   \n",
              "191594                                   interesting dude      0   \n",
              "191595  2020 brought selfawareness autopilot asshole s...      0   \n",
              "191596  2020 start tweeting positive news mcep every n...      0   \n",
              "191597             patiently waiting twitter sitter tweet      0   \n",
              "\n",
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              "4       0_trade_stock_nflx_model   \n",
              "...                          ...   \n",
              "191593  0_trade_stock_nflx_model   \n",
              "191594  0_trade_stock_nflx_model   \n",
              "191595  0_trade_stock_nflx_model   \n",
              "191596  0_trade_stock_nflx_model   \n",
              "191597  0_trade_stock_nflx_model   \n",
              "\n",
              "                                           Representation  \\\n",
              "0       [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "1       [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "2       [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "3       [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "4       [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "...                                                   ...   \n",
              "191593  [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "191594  [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "191595  [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "191596  [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "191597  [trade, stock, nflx, model, nvda, short, elon,...   \n",
              "\n",
              "                                      Representative_Docs  \\\n",
              "0       [dont miss next free option trade sign daily f...   \n",
              "1       [dont miss next free option trade sign daily f...   \n",
              "2       [dont miss next free option trade sign daily f...   \n",
              "3       [dont miss next free option trade sign daily f...   \n",
              "4       [dont miss next free option trade sign daily f...   \n",
              "...                                                   ...   \n",
              "191593  [dont miss next free option trade sign daily f...   \n",
              "191594  [dont miss next free option trade sign daily f...   \n",
              "191595  [dont miss next free option trade sign daily f...   \n",
              "191596  [dont miss next free option trade sign daily f...   \n",
              "191597  [dont miss next free option trade sign daily f...   \n",
              "\n",
              "                                              Top_n_words  Probability  \\\n",
              "0       trade - stock - nflx - model - nvda - short - ...     0.000000   \n",
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              "4       trade - stock - nflx - model - nvda - short - ...     0.000000   \n",
              "...                                                   ...          ...   \n",
              "191593  trade - stock - nflx - model - nvda - short - ...     0.879340   \n",
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              "0                         False  \n",
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              "4                         False  \n",
              "...                         ...  \n",
              "191593                    False  \n",
              "191594                    False  \n",
              "191595                    False  \n",
              "191596                    False  \n",
              "191597                    False  \n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-51fb8797-4254-4dce-8be3-c03ca65d3056\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-51fb8797-4254-4dce-8be3-c03ca65d3056')\"\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-51fb8797-4254-4dce-8be3-c03ca65d3056 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se actualizan las etiquetas estándar de los tópicos resultantes asignadas por el modelo a unas etiquetas custom que faciliten la interpretabilidad de los mismos"
      ],
      "metadata": {
        "id": "47soq4ZXs0iY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#custom labels\n",
        "labels_tesla={0: \"Inversiones y comparativa entre empresas\",\n",
        "        2: \"Análisis CNBC\",\n",
        "        3: \"Baillie Gifford\",\n",
        "        4: \"Conducción autónoma\",\n",
        "        5: \"Lotería\",\n",
        "        6:\"Ganancias Q3 2019\",\n",
        "              7:\"Comparativa BorgWarner\",\n",
        "              8:\"Consejos de inversión\",\n",
        "              9:\"Denuncias y acusaciones\",\n",
        "              10:\"Range Anxiety\",\n",
        "              11:\"Consolidación de acciones\",\n",
        "              12:\"Coches eléctricos\",\n",
        "              13:\"Especulación CNBC\",\n",
        "              14:\"Animadores\",\n",
        "              15:\"Lavado de coches\",\n",
        "              16:\"Postureo ético\"}\n",
        "modelo.set_topic_labels(labels_tesla)"
      ],
      "metadata": {
        "id": "PbmCWZv4F7nZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se representan en gráficos de barras los 16 tópicos ordenados por relevancia, en los cuales se obtienen los términos más repetidos por tópico y su puntuación c-TF-IDF"
      ],
      "metadata": {
        "id": "a6VAgnJatCZO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.visualize_barchart(top_n_topics=15,topics=[0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16], custom_labels=True, width=400)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Oy35R1bURuJQ",
        "outputId": "84ec1a57-15be-4340-f764-68a8f5c1f357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Se representa la matriz de similitud entre los 10 primeros tópicos más relevantes, para comprobar si son lo suficientemente distintos (valores de similitud menor o igual a 0.5)"
      ],
      "metadata": {
        "id": "pSMolg4gtMDh"
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      "source": [
        "modelo.visualize_heatmap(n_clusters=5, topics=[0,2,3,4,5,6,7,8,9,10],custom_labels=True, width=1200,height=1000)"
      ],
      "metadata": {
        "colab": {
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          "height": 1000
        },
        "id": "CBRA6aT4D6em",
        "outputId": "11f4f384-06b3-46f0-952f-6304dfdbad62"
      },
      "execution_count": null,
      "outputs": [
        {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se añaden las columnas con la categorización por tópico (número y etiqueta custom) y las palabras clave de cada uno en el dataset"
      ],
      "metadata": {
        "id": "cj1Bqs1fuoav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_tesla['Tópico']=modelo.get_document_info(data_tesla['body'])['Topic']\n",
        "data_tesla['Categoría']=modelo.get_document_info(data_tesla['body'])['CustomName']\n",
        "data_tesla['Palabras clave']=modelo.get_document_info(data_tesla['body'])['Top_n_words']"
      ],
      "metadata": {
        "id": "AnnfLbx_qFV7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se descartan los tweets que tratan sobre el tópico -1 (outlier), y los que acumulan tweets \"basura\" (1,17,18)"
      ],
      "metadata": {
        "id": "k6zA5-X7uvUy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#se descartan los tweets con el outlier y los tópicos con tweets basura\n",
        "data_tesla.drop(data_tesla[(data_tesla['Tópico'] ==-1)].index, inplace=True)\n",
        "data_tesla.drop(data_tesla[(data_tesla['Tópico'] ==1)].index, inplace=True)\n",
        "data_tesla.drop(data_tesla[(data_tesla['Tópico'] ==17)].index, inplace=True)\n",
        "data_tesla.drop(data_tesla[(data_tesla['Tópico'] ==18)].index, inplace=True)\n"
      ],
      "metadata": {
        "id": "dsrWPhMAqObI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se guarda el dataset con las nuevas columnas"
      ],
      "metadata": {
        "id": "UmPuRY3lu5jW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#se guarda para el analisis de sentimientos\n",
        "data_tesla.to_csv('/content/drive/MyDrive/TFG ADE/TeslaData20Topics.csv')"
      ],
      "metadata": {
        "id": "tzKmhBXBqTmb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_tesla.head(50)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GB4I8az8qf1Y",
        "outputId": "cac55b8e-7e73-49d3-dbd3-68cee6f8ece7"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "               tweet_id            post_date  \\\n",
              "0   1134610603715309569  2019-06-01 00:00:22   \n",
              "1   1134610685084688385  2019-06-01 00:00:42   \n",
              "2   1134610693133611011  2019-06-01 00:00:43   \n",
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              "4   1134610879994040320  2019-06-01 00:01:28   \n",
              "5   1134610946545127424  2019-06-01 00:01:44   \n",
              "6   1134611072411942912  2019-06-01 00:02:14   \n",
              "7   1134611235972849664  2019-06-01 00:02:53   \n",
              "8   1134611314452705280  2019-06-01 00:03:12   \n",
              "9   1134611457981784064  2019-06-01 00:03:46   \n",
              "10  1134611628975169537  2019-06-01 00:04:27   \n",
              "11  1134611845669691392  2019-06-01 00:05:18   \n",
              "12  1134611879123308544  2019-06-01 00:05:26   \n",
              "13  1134612054021693440  2019-06-01 00:06:08   \n",
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              "15  1134612287011065857  2019-06-01 00:07:03   \n",
              "16  1134612431068631040  2019-06-01 00:07:38   \n",
              "17  1134612734056771591  2019-06-01 00:08:50   \n",
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              "19  1134617480305860608  2019-06-01 00:27:42   \n",
              "20  1134618093903327232  2019-06-01 00:30:08   \n",
              "21  1134618511240835073  2019-06-01 00:31:47   \n",
              "22  1134618904003842048  2019-06-01 00:33:21   \n",
              "23  1134619016885133312  2019-06-01 00:33:48   \n",
              "24  1134619400428908545  2019-06-01 00:35:19   \n",
              "25  1134619481811144704  2019-06-01 00:35:39   \n",
              "26  1134619570508062725  2019-06-01 00:36:00   \n",
              "27  1134620307313086469  2019-06-01 00:38:56   \n",
              "28  1134621183188635648  2019-06-01 00:42:24   \n",
              "29  1134621896094420993  2019-06-01 00:45:14   \n",
              "30  1134622406025158656  2019-06-01 00:47:16   \n",
              "31  1134622579732504578  2019-06-01 00:47:57   \n",
              "32  1134622679632371714  2019-06-01 00:48:21   \n",
              "33  1134623498083737605  2019-06-01 00:51:36   \n",
              "34  1134623595253161984  2019-06-01 00:52:00   \n",
              "35  1134623639989604352  2019-06-01 00:52:10   \n",
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              "37  1134624363729248256  2019-06-01 00:55:03   \n",
              "38  1134624504754257920  2019-06-01 00:55:36   \n",
              "39  1134624724653228032  2019-06-01 00:56:29   \n",
              "40  1134624819742330880  2019-06-01 00:56:51   \n",
              "41  1134625294248239104  2019-06-01 00:58:45   \n",
              "42  1134625372178399233  2019-06-01 00:59:03   \n",
              "43  1134625694930079744  2019-06-01 01:00:20   \n",
              "44  1134625727377215490  2019-06-01 01:00:28   \n",
              "45  1134625837213372416  2019-06-01 01:00:54   \n",
              "46  1134625950212059136  2019-06-01 01:01:21   \n",
              "47  1134627145446240257  2019-06-01 01:06:06   \n",
              "48  1134628100300509186  2019-06-01 01:09:54   \n",
              "49  1134632160781053952  2019-06-01 01:26:02   \n",
              "\n",
              "                                                 body  Tópico  \\\n",
              "0   patent portfolio company combined self driving...       0   \n",
              "1   joint development driving system high automati...       0   \n",
              "2   suck never heard dealer able least find equiva...       0   \n",
              "3   elonmusk autopilot almost another twice servic...       0   \n",
              "4                                         paid spread       0   \n",
              "5   really elon fund recently hopefully spend afte...       0   \n",
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              "7   instead getting money long though much want ac...       0   \n",
              "8   great problemunfortunately customer report pro...       0   \n",
              "9                            statement reluctant data       0   \n",
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              "14                                  long gladly money       0   \n",
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              "18  false projectionsinvestor appearance hypergrow...       0   \n",
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              "20            active trade free trade guide sbux nvda       0   \n",
              "21                              pivotalcapital filing       0   \n",
              "22                        apparently short conflicted       0   \n",
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              "26                             youre wish leave alone       0   \n",
              "27  fan demand unlimited acknowledge price change ...       0   \n",
              "28  musk priced chinese model comparable chinese c...       0   \n",
              "29  industry choking offering halfbaked electric m...       0   \n",
              "30   weekly volatility spread widening upcoming crush       0   \n",
              "31                            review meme boriquagato       0   \n",
              "32  risk stating obvious producing car sell expand...       0   \n",
              "33  saga show inefficient market aretslaq bear sta...       0   \n",
              "34        bull supporter buying management board sell       0   \n",
              "35  updated cheapr rebate stats include april sold...       0   \n",
              "36  executive listed websiteone never show workthe...       0   \n",
              "37                                       lie lie elon       0   \n",
              "38     drive phone location follow hold summon button       0   \n",
              "39  several homicide case based angry wife killing...       0   \n",
              "40  village midst busy sidewalk sale checked store...       0   \n",
              "41                                          fcau agco       0   \n",
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              "46                                future elonmusktsla       0   \n",
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              "\n",
              "                                   Categoría  \\\n",
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              "1   Inversiones y comparativa entre empresas   \n",
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              "3   Inversiones y comparativa entre empresas   \n",
              "4   Inversiones y comparativa entre empresas   \n",
              "5   Inversiones y comparativa entre empresas   \n",
              "6   Inversiones y comparativa entre empresas   \n",
              "7   Inversiones y comparativa entre empresas   \n",
              "8   Inversiones y comparativa entre empresas   \n",
              "9   Inversiones y comparativa entre empresas   \n",
              "10  Inversiones y comparativa entre empresas   \n",
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              "12  Inversiones y comparativa entre empresas   \n",
              "13  Inversiones y comparativa entre empresas   \n",
              "14  Inversiones y comparativa entre empresas   \n",
              "15  Inversiones y comparativa entre empresas   \n",
              "16  Inversiones y comparativa entre empresas   \n",
              "17  Inversiones y comparativa entre empresas   \n",
              "18  Inversiones y comparativa entre empresas   \n",
              "19  Inversiones y comparativa entre empresas   \n",
              "20  Inversiones y comparativa entre empresas   \n",
              "21  Inversiones y comparativa entre empresas   \n",
              "22  Inversiones y comparativa entre empresas   \n",
              "23  Inversiones y comparativa entre empresas   \n",
              "24  Inversiones y comparativa entre empresas   \n",
              "25  Inversiones y comparativa entre empresas   \n",
              "26  Inversiones y comparativa entre empresas   \n",
              "27  Inversiones y comparativa entre empresas   \n",
              "28  Inversiones y comparativa entre empresas   \n",
              "29  Inversiones y comparativa entre empresas   \n",
              "30  Inversiones y comparativa entre empresas   \n",
              "31  Inversiones y comparativa entre empresas   \n",
              "32  Inversiones y comparativa entre empresas   \n",
              "33  Inversiones y comparativa entre empresas   \n",
              "34  Inversiones y comparativa entre empresas   \n",
              "35  Inversiones y comparativa entre empresas   \n",
              "36  Inversiones y comparativa entre empresas   \n",
              "37  Inversiones y comparativa entre empresas   \n",
              "38  Inversiones y comparativa entre empresas   \n",
              "39  Inversiones y comparativa entre empresas   \n",
              "40  Inversiones y comparativa entre empresas   \n",
              "41  Inversiones y comparativa entre empresas   \n",
              "42  Inversiones y comparativa entre empresas   \n",
              "43  Inversiones y comparativa entre empresas   \n",
              "44  Inversiones y comparativa entre empresas   \n",
              "45  Inversiones y comparativa entre empresas   \n",
              "46  Inversiones y comparativa entre empresas   \n",
              "47  Inversiones y comparativa entre empresas   \n",
              "48  Inversiones y comparativa entre empresas   \n",
              "49  Inversiones y comparativa entre empresas   \n",
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              "39  trade - stock - nflx - model - nvda - short - ...  \n",
              "40  trade - stock - nflx - model - nvda - short - ...  \n",
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              "      <th>0</th>\n",
              "      <td>1134610603715309569</td>\n",
              "      <td>2019-06-01 00:00:22</td>\n",
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              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "      <th>1</th>\n",
              "      <td>1134610685084688385</td>\n",
              "      <td>2019-06-01 00:00:42</td>\n",
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              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "      <th>2</th>\n",
              "      <td>1134610693133611011</td>\n",
              "      <td>2019-06-01 00:00:43</td>\n",
              "      <td>suck never heard dealer able least find equiva...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
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              "      <td>elonmusk autopilot almost another twice servic...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "      <th>4</th>\n",
              "      <td>1134610879994040320</td>\n",
              "      <td>2019-06-01 00:01:28</td>\n",
              "      <td>paid spread</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "      <th>5</th>\n",
              "      <td>1134610946545127424</td>\n",
              "      <td>2019-06-01 00:01:44</td>\n",
              "      <td>really elon fund recently hopefully spend afte...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1134611072411942912</td>\n",
              "      <td>2019-06-01 00:02:14</td>\n",
              "      <td>doesnt sound safe recommendation service cente...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1134611235972849664</td>\n",
              "      <td>2019-06-01 00:02:53</td>\n",
              "      <td>instead getting money long though much want ac...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1134611314452705280</td>\n",
              "      <td>2019-06-01 00:03:12</td>\n",
              "      <td>great problemunfortunately customer report pro...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1134611457981784064</td>\n",
              "      <td>2019-06-01 00:03:46</td>\n",
              "      <td>statement reluctant data</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1134611628975169537</td>\n",
              "      <td>2019-06-01 00:04:27</td>\n",
              "      <td>fraudulent paid combined million fine illegal ...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1134611845669691392</td>\n",
              "      <td>2019-06-01 00:05:18</td>\n",
              "      <td>unfortunately issueteslaserviceissues whereare...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1134611879123308544</td>\n",
              "      <td>2019-06-01 00:05:26</td>\n",
              "      <td>harem delivered summon although longer afford</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1134612054021693440</td>\n",
              "      <td>2019-06-01 00:06:08</td>\n",
              "      <td>wasnt expected guided changed price configurat...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1134612150486523905</td>\n",
              "      <td>2019-06-01 00:06:31</td>\n",
              "      <td>long gladly money</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1134612287011065857</td>\n",
              "      <td>2019-06-01 00:07:03</td>\n",
              "      <td>youre patient individual however solar company...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1134612431068631040</td>\n",
              "      <td>2019-06-01 00:07:38</td>\n",
              "      <td>insurance must approached filing waste money c...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1134612734056771591</td>\n",
              "      <td>2019-06-01 00:08:50</td>\n",
              "      <td>bummer almost theyve shut solar division least...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1134616881610887168</td>\n",
              "      <td>2019-06-01 00:25:19</td>\n",
              "      <td>false projectionsinvestor appearance hypergrow...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1134617480305860608</td>\n",
              "      <td>2019-06-01 00:27:42</td>\n",
              "      <td>disingenuous connected vested interest custome...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1134618093903327232</td>\n",
              "      <td>2019-06-01 00:30:08</td>\n",
              "      <td>active trade free trade guide sbux nvda</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1134618511240835073</td>\n",
              "      <td>2019-06-01 00:31:47</td>\n",
              "      <td>pivotalcapital filing</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1134618904003842048</td>\n",
              "      <td>2019-06-01 00:33:21</td>\n",
              "      <td>apparently short conflicted</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1134619016885133312</td>\n",
              "      <td>2019-06-01 00:33:48</td>\n",
              "      <td>moment institution realize theyve waiting real...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1134619400428908545</td>\n",
              "      <td>2019-06-01 00:35:19</td>\n",
              "      <td>perfect time stack</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1134619481811144704</td>\n",
              "      <td>2019-06-01 00:35:39</td>\n",
              "      <td>read financials investing auto issue sell enou...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1134619570508062725</td>\n",
              "      <td>2019-06-01 00:36:00</td>\n",
              "      <td>youre wish leave alone</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1134620307313086469</td>\n",
              "      <td>2019-06-01 00:38:56</td>\n",
              "      <td>fan demand unlimited acknowledge price change ...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1134621183188635648</td>\n",
              "      <td>2019-06-01 00:42:24</td>\n",
              "      <td>musk priced chinese model comparable chinese c...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1134621896094420993</td>\n",
              "      <td>2019-06-01 00:45:14</td>\n",
              "      <td>industry choking offering halfbaked electric m...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1134622406025158656</td>\n",
              "      <td>2019-06-01 00:47:16</td>\n",
              "      <td>weekly volatility spread widening upcoming crush</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1134622579732504578</td>\n",
              "      <td>2019-06-01 00:47:57</td>\n",
              "      <td>review meme boriquagato</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1134622679632371714</td>\n",
              "      <td>2019-06-01 00:48:21</td>\n",
              "      <td>risk stating obvious producing car sell expand...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1134623498083737605</td>\n",
              "      <td>2019-06-01 00:51:36</td>\n",
              "      <td>saga show inefficient market aretslaq bear sta...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1134623595253161984</td>\n",
              "      <td>2019-06-01 00:52:00</td>\n",
              "      <td>bull supporter buying management board sell</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1134623639989604352</td>\n",
              "      <td>2019-06-01 00:52:10</td>\n",
              "      <td>updated cheapr rebate stats include april sold...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1134624351070883840</td>\n",
              "      <td>2019-06-01 00:55:00</td>\n",
              "      <td>executive listed websiteone never show workthe...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1134624363729248256</td>\n",
              "      <td>2019-06-01 00:55:03</td>\n",
              "      <td>lie lie elon</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1134624504754257920</td>\n",
              "      <td>2019-06-01 00:55:36</td>\n",
              "      <td>drive phone location follow hold summon button</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1134624724653228032</td>\n",
              "      <td>2019-06-01 00:56:29</td>\n",
              "      <td>several homicide case based angry wife killing...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1134624819742330880</td>\n",
              "      <td>2019-06-01 00:56:51</td>\n",
              "      <td>village midst busy sidewalk sale checked store...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1134625294248239104</td>\n",
              "      <td>2019-06-01 00:58:45</td>\n",
              "      <td>fcau agco</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1134625372178399233</td>\n",
              "      <td>2019-06-01 00:59:03</td>\n",
              "      <td>exactly path</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1134625694930079744</td>\n",
              "      <td>2019-06-01 01:00:20</td>\n",
              "      <td>trend matter fashion trade click check lloy</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1134625727377215490</td>\n",
              "      <td>2019-06-01 01:00:28</td>\n",
              "      <td>dont miss next free option trade sign daily fr...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1134625837213372416</td>\n",
              "      <td>2019-06-01 01:00:54</td>\n",
              "      <td>laugh hellhad bos brought legitimate risk fact...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1134625950212059136</td>\n",
              "      <td>2019-06-01 01:01:21</td>\n",
              "      <td>future elonmusktsla</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1134627145446240257</td>\n",
              "      <td>2019-06-01 01:06:06</td>\n",
              "      <td>check neiman marcus christmas ornament bronzeg...</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1134628100300509186</td>\n",
              "      <td>2019-06-01 01:09:54</td>\n",
              "      <td>credit default swap 5yrs</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1134632160781053952</td>\n",
              "      <td>2019-06-01 01:26:02</td>\n",
              "      <td>car allowed road avoid death machine</td>\n",
              "      <td>0</td>\n",
              "      <td>Inversiones y comparativa entre empresas</td>\n",
              "      <td>trade - stock - nflx - model - nvda - short - ...</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-b4a477b9-44aa-437b-a9b2-7623ae478b8b')\"\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",
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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",
              "      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-b4a477b9-44aa-437b-a9b2-7623ae478b8b 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-b4a477b9-44aa-437b-a9b2-7623ae478b8b');\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-c3376336-1e3b-458f-ab6e-24f652770846\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3376336-1e3b-458f-ab6e-24f652770846')\"\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",
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              "        <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-c3376336-1e3b-458f-ab6e-24f652770846 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se obtiene la variación frecuencia por tópico a lo largo de la franja temporal de estudio"
      ],
      "metadata": {
        "id": "ySkRPh6tvHeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#cambiar a mes\n",
        "data_tesla['post_date_only']= pd.to_datetime(data_tesla['post_date']).dt.date\n",
        "modelo = BERTopic.load('/content/drive/MyDrive/TFG ADE/teslatopic20')\n",
        "topics_over_time = modelo.topics_over_time(data_tesla['body'].astype(str), data_tesla['post_date_only'])"
      ],
      "metadata": {
        "id": "diDzpL9Nizhl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.visualize_topics_over_time(topics_over_time, topics=[0, 2, 3, 4,5], custom_labels=True, width=1500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "S-rfz9S9lSYb",
        "outputId": "bc668ec3-241b-443d-a04a-ec027274ccf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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accelerates, precision, valueinvesting, corp\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vaporware, essentialsince, employee, doug\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vapor, wear, compare, working, zero\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, impasse, syncedglobal, vaporware, sift\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, 2own, renderingsrus, rendered, render\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: savingforlater, sarcastic, cracking, projection, delivery\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: futureware, pixar, rendering, stepping, must\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vaporware, connectedvehicles, insur, serv\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, multi, fatality, diethere, erikaathanas\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, impression, label, exists, promote\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: physician, juke, statscredible, unchallengeable, tautology\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vaporware, supercomputer, falseclaims, interpret\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: cash, vaporware, principle, purchased, recognize\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vision, waymo, rendering, unecessarytesla\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vaporware, recog, zero, render\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vaporwear, anymoretslaq, vaporware, deepscales\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vaporware, deepscales, dangerousif, generosity\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, workthat, guise, thought, scrambling\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: darwinisn, 65mph, vaporware, drum, kit\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, prob, blowhard, remmber, eyesorsilicon\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, uglier, functionality, vaporware, vision\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: forked, collectively, vaporware, 1mitchell, congratulation\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vapor, bollingermotors, rendering, teslakillers, car\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: ware, vapor, begging, honestly, straight\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, semiwhy, semithe, inventing, withtsla\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, silly, underinvestment, 2018, 2017\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, funny, youi, fleecing, enrich\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: scheme, selfidentifying, determining, expires, fiery\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lyftwaymogoogle, photon, alone, lidar, ttmygh\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, rendering, ooplease, challengestsla, overlooking\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: lidar, vision, approach, costhence, pathand\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: hoax, lidar, vaporware, myth, told\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: videojust, vaporware, puppet, stunt, literally\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: rendering, cooler, sign, inspired, greatly\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, pub, credulous, techcrunch, publication\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vapourware, dildo, cave, construction, marketing\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: enlightening, lidar, vaporware, autonomy, lie\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: gigacharlatans, vaporware, antic, shocked, shill\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: render, designer, truck, closer, cyber\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: renderingtsla, lidar, vaporware, rendering, render\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: vaporware, slow, lidar, nolidar, deathwatch\",\"\\u003cb\\u003eTopic 4\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: hrsstop, vaporware, render, posting, lidar\",\"\\u003cb\\u003eTopic 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      "source": [
        "modelo.visualize_topics_over_time(topics_over_time, topics=[6,7, 8, 9, 10], custom_labels=True, width=1500)"
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employeesrushed, wronganother\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: narcissistic, rage, depose, epic, child\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: anythingwhy, affective, disorder, note, boss\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: sociopath, achieving, high, disagree, match\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: pumpkin, bowling, uncarved, hollow, hail\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: mikm, displayed, habit, myth, prof\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: upending, citizenship, thiel, zealand, peter\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: hbodocs, documentary, upcoming, material, fraud\",\"\\u003cb\\u003eTopic 10\\u003c\\u002fb\\u003e\\u003cbr\\u003eWords: illiteracy, thrives, sociopath, functioning, financial\",\"\\u003cb\\u003eTopic 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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.visualize_topics_over_time(topics_over_time, topics=[11,12,13, 14, 15, 16], custom_labels=True, width=1500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "cQN4qlxNlrxn",
        "outputId": "c29d419c-44f2-4ae6-f555-e0baf091d3e3"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se exporta la evolución temporal por tópico para representarla en otra herramienta"
      ],
      "metadata": {
        "id": "kiwIPonyvMAl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# evolucion temporal topicos\n",
        "data_tesla['post_date'] = pd.to_datetime(data_tesla['post_date'])\n",
        "topics = [0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
        "\n",
        "for topic in topics:\n",
        "  topic_tweets = data_tesla[data_tesla['Tópico'] == topic]\n",
        "\n",
        "  # Group the data by day and count the number of tweets per day\n",
        "  t_per_day = topic_tweets.resample('D', on='post_date').count()\n",
        "  columns_to_delete = ['body',  'Categoría', 'Palabras clave', 'Unnamed: 0', 'tweet_id']\n",
        "  t_per_day = t_per_day.drop(columns=columns_to_delete)\n",
        "  t_per_day = t_per_day.rename(columns={'Tópico': f'Tweets diarios sobre tópico {topic}'})\n",
        "  t_per_day.to_csv(f'drive/MyDrive/TFG ADE/tesla{topic}.csv')"
      ],
      "metadata": {
        "id": "l9yBoPCliCLG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}