Analisis_Alcalde_TW_20_03_19.ipynb

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  "metadata": {
    "colab": {
      "name": "Analisis_Alcalde_TW_20_03_19",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "_LGaah-qjwIf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0s_KX5XxjTk8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from fastai.text import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AySIHsJakCK1",
        "colab_type": "code",
        "outputId": "86a5726e-f2f1-4eb8-fb66-d236d42426ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aWjIuHhKkaVq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "path = '/content/drive/My Drive/analisis_alcalde_2019/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "43syrNwnlAc5",
        "colab_type": "code",
        "outputId": "07297002-6c09-45dd-e815-91fca136044d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path+\"tweets 21.csv\")\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>creado_en</th>\n",
              "      <th>nombreusuario</th>\n",
              "      <th>nombre</th>\n",
              "      <th>creacion_usuario</th>\n",
              "      <th>seguidores</th>\n",
              "      <th>siguiendo</th>\n",
              "      <th>numero_tweets</th>\n",
              "      <th>geolocalziacion_habilitada</th>\n",
              "      <th>idioma</th>\n",
              "      <th>...</th>\n",
              "      <th>BermudezBronson</th>\n",
              "      <th>balerioestacio</th>\n",
              "      <th>inca_jose</th>\n",
              "      <th>EduardoArgudoG</th>\n",
              "      <th>fcojimenez21</th>\n",
              "      <th>patobuendia23</th>\n",
              "      <th>jaimelomas74</th>\n",
              "      <th>Simon_BolivarEC</th>\n",
              "      <th>edgarsalazar_51</th>\n",
              "      <th>jaimenebotsaadi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1105923462068088832</td>\n",
              "      <td>Wed Mar 13 20:07:55 +0000 2019</td>\n",
              "      <td>Christianoleas2</td>\n",
              "      <td>Christianoleas</td>\n",
              "      <td>Sat Dec 15 04:46:05 +0000 2018</td>\n",
              "      <td>0</td>\n",
              "      <td>74</td>\n",
              "      <td>122</td>\n",
              "      <td>False</td>\n",
              "      <td>es</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1105923594482319363</td>\n",
              "      <td>Wed Mar 13 20:08:26 +0000 2019</td>\n",
              "      <td>miguel3vargas</td>\n",
              "      <td>miguel vargas cornej</td>\n",
              "      <td>Sun Nov 13 21:03:03 +0000 2016</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>333</td>\n",
              "      <td>False</td>\n",
              "      <td>es</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1105924416641396739</td>\n",
              "      <td>Wed Mar 13 20:11:42 +0000 2019</td>\n",
              "      <td>ofelitavera</td>\n",
              "      <td>Ab. Ofelia Vera</td>\n",
              "      <td>Tue Apr 17 14:56:08 +0000 2012</td>\n",
              "      <td>441</td>\n",
              "      <td>358</td>\n",
              "      <td>9072</td>\n",
              "      <td>False</td>\n",
              "      <td>es</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1105924428838387712</td>\n",
              "      <td>Wed Mar 13 20:11:45 +0000 2019</td>\n",
              "      <td>GORRASGYE</td>\n",
              "      <td>JOSHUA F</td>\n",
              "      <td>Sat Jan 26 20:04:59 +0000 2013</td>\n",
              "      <td>4092</td>\n",
              "      <td>4373</td>\n",
              "      <td>22586</td>\n",
              "      <td>True</td>\n",
              "      <td>es</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1105924978741071872</td>\n",
              "      <td>Wed Mar 13 20:13:56 +0000 2019</td>\n",
              "      <td>PatricioPlazaD9</td>\n",
              "      <td>Patricio Plaza Daza</td>\n",
              "      <td>Tue Dec 05 02:05:23 +0000 2017</td>\n",
              "      <td>45</td>\n",
              "      <td>395</td>\n",
              "      <td>1869</td>\n",
              "      <td>False</td>\n",
              "      <td>es</td>\n",
              "      <td>...</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    ID                       creado_en    nombreusuario  \\\n",
              "0  1105923462068088832  Wed Mar 13 20:07:55 +0000 2019  Christianoleas2   \n",
              "1  1105923594482319363  Wed Mar 13 20:08:26 +0000 2019    miguel3vargas   \n",
              "2  1105924416641396739  Wed Mar 13 20:11:42 +0000 2019      ofelitavera   \n",
              "3  1105924428838387712  Wed Mar 13 20:11:45 +0000 2019        GORRASGYE   \n",
              "4  1105924978741071872  Wed Mar 13 20:13:56 +0000 2019  PatricioPlazaD9   \n",
              "\n",
              "                 nombre                creacion_usuario  seguidores  \\\n",
              "0        Christianoleas  Sat Dec 15 04:46:05 +0000 2018           0   \n",
              "1  miguel vargas cornej  Sun Nov 13 21:03:03 +0000 2016           0   \n",
              "2       Ab. Ofelia Vera  Tue Apr 17 14:56:08 +0000 2012         441   \n",
              "3              JOSHUA F  Sat Jan 26 20:04:59 +0000 2013        4092   \n",
              "4   Patricio Plaza Daza  Tue Dec 05 02:05:23 +0000 2017          45   \n",
              "\n",
              "   siguiendo  numero_tweets  geolocalziacion_habilitada idioma  \\\n",
              "0         74            122                       False     es   \n",
              "1         58            333                       False     es   \n",
              "2        358           9072                       False     es   \n",
              "3       4373          22586                        True     es   \n",
              "4        395           1869                       False     es   \n",
              "\n",
              "        ...        BermudezBronson balerioestacio inca_jose EduardoArgudoG  \\\n",
              "0       ...                  False          False     False          False   \n",
              "1       ...                  False          False     False          False   \n",
              "2       ...                  False          False     False          False   \n",
              "3       ...                  False          False     False          False   \n",
              "4       ...                  False          False     False          False   \n",
              "\n",
              "   fcojimenez21  patobuendia23  jaimelomas74  Simon_BolivarEC  \\\n",
              "0         False          False         False            False   \n",
              "1         False          False         False            False   \n",
              "2         False          False         False            False   \n",
              "3         False          False         False            False   \n",
              "4         False          False         False            False   \n",
              "\n",
              "   edgarsalazar_51  jaimenebotsaadi  \n",
              "0            False             True  \n",
              "1            False             True  \n",
              "2            False            False  \n",
              "3            False            False  \n",
              "4            False            False  \n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "rCzOYs-4m490",
        "colab_type": "code",
        "outputId": "6a003d22-76a8-4921-e25f-ea0977ec9037",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "print(df.columns)\n",
        "print(df.shape)\n",
        "print(df.ID.count())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['ID', 'creado_en', 'nombreusuario', 'nombre', 'creacion_usuario',\n",
            "       'seguidores', 'siguiendo', 'numero_tweets',\n",
            "       'geolocalziacion_habilitada', 'idioma', 'origen', 'idioma_del_tweet',\n",
            "       'tweet', 'hashtags', 'es_respuesta', 'jimmyjairala', 'cedeno_marth',\n",
            "       'jorgevillacrese', 'CynthiaViteri6', 'GinoCornejoM', 'BermudezBronson',\n",
            "       'balerioestacio', 'inca_jose', 'EduardoArgudoG', 'fcojimenez21',\n",
            "       'patobuendia23', 'jaimelomas74', 'Simon_BolivarEC', 'edgarsalazar_51',\n",
            "       'jaimenebotsaadi'],\n",
            "      dtype='object')\n",
            "(11230, 30)\n",
            "11230\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jEn3YOICnCl9",
        "colab_type": "code",
        "outputId": "4f5db2e2-3377-4ef6-ad36-96a5ef78fa93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "cell_type": "code",
      "source": [
        "tweets_vs_retweet = [int(df[~df['tweet'].astype(str).str.startswith('RT')].ID.count()), int(df.ID.count())]\n",
        "plt.pie(tweets_vs_retweet, labels=(\"Tweets \" + str(int(tweets_vs_retweet[0]/(tweets_vs_retweet[0]+tweets_vs_retweet[1])*100)) +\"%\" ,\"Retweets \"+ str(int(tweets_vs_retweet[1]/(tweets_vs_retweet[0]+tweets_vs_retweet[1])*100)) +\"%\"))\n",
        "plt.title('Tweet vs Retweet')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HOQT16/as2WK5NdiPgbjt31jMblEV5cWd8lU1V02oHz/Pjnf2ZyLwLd8heiP25U0Xsifh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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wCzp3nUR2kh8",
        "colab_type": "code",
        "outputId": "b9f14e78-9310-4396-c235-3ef6027e9e27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        }
      },
      "cell_type": "code",
      "source": [
        "number_of_followers = [sum(df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()==0),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<11) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>0)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<51) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>10)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<101) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>50)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<151) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>100)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<251) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>150)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<501) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>250)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>500))]\n",
        "plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "number = [\"0\",\"10\",\"50\",\"100\",\"150\",\"250\",\"500\",\">500\"]\n",
        "plt.ylabel(\"Cantidad de usuarios\")\n",
        "plt.xlabel(\"Número de seguidores\")\n",
        "plt.bar(number, height=number_of_followers)\n",
        "plt.title('Número de seguidores por usuario')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9AvAuV1KI7+oryk466aScc845GTNmzD4fCgAAAKpVSSdr25UtW7Zk2bJl+3IW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\n",
            "text/plain": [
              "<Figure size 1200x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FwMsuRY3p0fY",
        "colab_type": "code",
        "outputId": "2f5e5ee2-8fc8-4194-d52e-e2ef62034fe5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "number_of_followers = [sum(df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()==0),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<11) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>0)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<51) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>10)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<101) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>50)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<151) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>100)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<251) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>150)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()<501) & (df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>250)),\n",
        "                      sum((df[\"seguidores\"].groupby(df[\"nombreusuario\"]).max()>500))]\n",
        "plt.figure(figsize=(10, 5), dpi= 80, facecolor='w', edgecolor='k')\n",
        "number = [\"0\",\"10\",\"50\",\"100\",\"150\",\"250\",\"500\",\">500\"]\n",
        "plt.bar(number, height=number_of_followers)\n",
        "plt.title('Númeero de seguidores por usuario')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAGAVhRMAAABWUTgBAABgFYUTAAAAVlE4AQAAYBWFEwAAAFZROAEAAGAVhRMAAABWUTgB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\nA/xLMn8KC2DQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "P7zKRg3W3-9H",
        "colab_type": "code",
        "outputId": "66086ba6-3d81-4fd7-b44f-6f15d0784c5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "df_ns = df.loc[df['jaimenebotsaadi'] == True]\n",
        "number_of_followers = [sum(df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()==0),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<11) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>0)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<51) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>10)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<101) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>50)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<151) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>100)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<251) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>150)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<501) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>250)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>500))]\n",
        "plt.figure(figsize=(10, 5), dpi= 80, facecolor='w', edgecolor='k')\n",
        "number = [\"0\",\"10\",\"50\",\"100\",\"150\",\"250\",\"500\",\">500\"]\n",
        "plt.bar(number, height=number_of_followers)\n",
        "plt.title('Número de seguidores por usuario que mencionan a @jaimenebotsaadi')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wBAAAgC0IlgAAALAFwRIAAAC2IFgCAADAFgRLAAAA2IJgCQAAAFsQLAEAAGAL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\nIFgCAADAFgRLAAAA2IJgCQAAAFv8f7CY3r4fBY5aAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "OTrbL7_W2t8u",
        "colab_type": "code",
        "outputId": "5b3303e1-063d-4795-b549-ef644558ed46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "df_ns = df.loc[df['jimmyjairala'] == True]\n",
        "number_of_followers = [sum(df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()==0),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<11) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>0)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<51) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>10)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<101) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>50)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<151) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>100)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<251) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>150)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<501) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>250)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>500))]\n",
        "plt.figure(figsize=(10, 5), dpi= 80, facecolor='w', edgecolor='k')\n",
        "number = [\"0\",\"10\",\"50\",\"100\",\"150\",\"250\",\"500\",\">500\"]\n",
        "plt.bar(number, height=number_of_followers)\n",
        "plt.title('Número de seguidores por usuario que mencionan a @jimmyjairala')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JJxo0aJCcTqeCgoI0ffp0/eUvf1F8fLwaNmyoBg0aaOrUqZKkWbNmKSUlRQ6H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\nAMAWFEsAAADYgmIJAAAAW/z/SH3uCjq0gKcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FoxCHz6-32P4",
        "colab_type": "code",
        "outputId": "dc91c514-53bc-417a-cf8f-010d5d2c78c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "df_ns = df.loc[df['CynthiaViteri6'] == True]\n",
        "number_of_followers = [sum(df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()==0),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<11) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>0)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<51) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>10)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<101) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>50)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<151) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>100)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<251) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>150)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<501) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>250)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>500))]\n",
        "plt.figure(figsize=(10, 5), dpi= 80, facecolor='w', edgecolor='k')\n",
        "number = [\"0\",\"10\",\"50\",\"100\",\"150\",\"250\",\"500\",\">500\"]\n",
        "plt.bar(number, height=number_of_followers)\n",
        "plt.title('Número de seguidores por usuario que mencionan a @CynthiaViteri6')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WBEsAAADYgmAJAAAAWxAsAQAAYAuCJQAAAGxBsAQAAIAtCJYAAACwBcESAAAA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\nMj9LYlIAgAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ieEOBIgP44mf",
        "colab_type": "code",
        "outputId": "f98c6450-7d03-42ae-bd6d-4bf7875ebbc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "df_ns = df.loc[df['fcojimenez21'] == True]\n",
        "number_of_followers = [sum(df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()==0),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<11) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>0)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<51) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>10)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<101) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>50)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<151) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>100)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<251) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>150)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()<501) & (df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>250)),\n",
        "                      sum((df_ns[\"seguidores\"].groupby(df_ns[\"nombreusuario\"]).max()>500))]\n",
        "plt.figure(figsize=(10, 5), dpi= 80, facecolor='w', edgecolor='k')\n",
        "number = [\"0\",\"10\",\"50\",\"100\",\"150\",\"250\",\"500\",\">500\"]\n",
        "plt.bar(number, height=number_of_followers)\n",
        "plt.title('Número de seguidores por usuario que mencionan a @fcojimenez21')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XZB8rzsGDBzVjxgzntGpWVtY5HzzO7NWZPK3P2c7eXkFBQaVet8zMTE2bNk1z5sxx7qta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\nAGCFAAkAAAAr/wvot0m78+XC+gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZNsadDu-rbnE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Only tweets no retweets\n",
        "df = df[~df['tweet'].astype(str).str.startswith('RT')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aBsNuJiyc3w5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# tweet with only one mention \n",
        "df[df[['jimmyjairala', 'cedeno_marth',\n",
        "       'jorgevillacrese', 'CynthiaViteri6', 'GinoCornejoM', 'BermudezBronson',\n",
        "       'balerioestacio', 'inca_jose', 'EduardoArgudoG', 'fcojimenez21',\n",
        "       'patobuendia23', 'jaimelomas74', 'Simon_BolivarEC', 'edgarsalazar_51',\n",
        "       'jaimenebotsaadi']].sum(axis=1)==1].to_csv(path+\"noRTOneMetion.csv\", encoding='utf-8')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4vPiIGT8u7V7",
        "colab_type": "code",
        "outputId": "ad287c0f-4557-446f-99e1-77aac2043df0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "n_mention = [df.jimmyjairala.sum(), df.cedeno_marth.sum(), df.jorgevillacrese.sum(),\n",
        "             df.CynthiaViteri6.sum(), df.GinoCornejoM.sum(), df.BermudezBronson.sum(),\n",
        "             df.balerioestacio.sum(), df.inca_jose.sum(), df.EduardoArgudoG.sum(),\n",
        "             df.fcojimenez21.sum(), df.patobuendia23.sum(), df.jaimelomas74.sum(),\n",
        "             df.Simon_BolivarEC.sum(), df.edgarsalazar_51.sum(), df.jaimenebotsaadi.sum()]\n",
        "username  = ('jimmyjairala', 'cedeno_marth',\n",
        "       'jorgevillacrese', 'CynthiaViteri6', 'GinoCornejoM', 'BermudezBronson',\n",
        "       'balerioestacio', 'inca_jose', 'EduardoArgudoG', 'fcojimenez21',\n",
        "       'patobuendia23', 'jaimelomas74', 'Simon_BolivarEC', 'edgarsalazar_51',\n",
        "        'jaimenebotsaadi')\n",
        "plt.barh(username, width=n_mention, left=0)\n",
        "plt.title('Número de menciones')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1200x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "AvfRpaTj-T_O",
        "colab_type": "code",
        "outputId": "3e45efdf-d4f3-4f0a-8a3e-209df9973334",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "time = [\"0h00\",\"1h00\",\"2h00\",\"3h00\",\"4h00\",\"05h00\",\"6h00\",\"7h00\",\"8h00\",\n",
        "       \"9h00\",\"10h00\",\"11h00\",\"12h00\",\"13h00\",\"14h00\",\"15h00\",\"16h00\",\n",
        "       \"17h00\",\"18h00\",\"19h00\",\"20h00\",\"21h00\",\"22h00\",\"23h00\"]\n",
        "plt.bar(time, height=df.groupby([pd.to_datetime(df.creado_en).dt.hour]).size())\n",
        "plt.title('Tweets/Hora')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hJ5xwQvG+kSNHvm7ZXnvtlblz576xZAAAANCLXJYaAAAAChRmAAAAKFCYAQAA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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1200x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "mbnKZGrenT7Q",
        "colab_type": "code",
        "outputId": "83daa9aa-1f62-4287-bc93-96d0475bc773",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "time = [\"0h00\",\"1h00\",\"2h00\",\"3h00\",\"4h00\",\"05h00\",\"6h00\",\"7h00\",\"8h00\",\n",
        "       \"9h00\",\"10h00\",\"11h00\",\"12h00\",\"13h00\",\"14h00\",\"15h00\",\"16h00\",\n",
        "       \"17h00\",\"18h00\",\"19h00\",\"20h00\",\"21h00\",\"22h00\",\"23h00\"]\n",
        "plt.bar(time, height=df.groupby([pd.to_datetime(df.loc[df['jaimenebotsaadi'] == True].creado_en).dt.hour]).size())\n",
        "plt.title('@jaimenebotsaadi/Hora')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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376xYsSK77777Zo/fv/9OaWz0R6rrDBzYt6sTNlFST0e1DBhQzm1M9GxJSS1J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\nCGT4Fxnx+Xpb2jJLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1200x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GYQmbtGW6AyU",
        "colab_type": "code",
        "outputId": "a436ed7b-65fd-4c54-f545-7f4604c013ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "time = [\"0h00\",\"1h00\",\"2h00\",\"3h00\",\"4h00\",\"05h00\",\"6h00\",\"7h00\",\"8h00\",\n",
        "       \"9h00\",\"10h00\",\"11h00\",\"12h00\",\"13h00\",\"14h00\",\"15h00\",\"16h00\",\n",
        "       \"17h00\",\"18h00\",\"19h00\",\"20h00\",\"21h00\",\"22h00\",\"23h00\"]\n",
        "plt.bar(time, height=df.groupby([pd.to_datetime(df.loc[df['jimmyjairala'] == True].creado_en).dt.hour]).size())\n",
        "plt.title('@jimmyjairala/Hora')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PPz677rpr9thjjxx55JHp27dv3ve+92XChAlJkquvvjqXXXZZWlpa0qdPn0ya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\nhBBCCKHiHy2PM9DBm0tOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1200x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yAZZuhM5lnyk",
        "colab_type": "code",
        "outputId": "27ce8042-cbd5-4bc6-f57d-ade4b3204ecf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "time = [\"0h00\",\"1h00\",\"2h00\",\"3h00\",\"4h00\",\"05h00\",\"6h00\",\"7h00\",\"8h00\",\n",
        "       \"9h00\",\"10h00\",\"11h00\",\"12h00\",\"13h00\",\"14h00\",\"15h00\",\"16h00\",\n",
        "       \"17h00\",\"18h00\",\"19h00\",\"20h00\",\"21h00\",\"22h00\",\"23h00\"]\n",
        "plt.bar(time, height=df.groupby([pd.to_datetime(df.loc[df['CynthiaViteri6'] == True].creado_en).dt.hour]).size())\n",
        "plt.title('@CynthiaViteri6/Hora')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sd7/73ZkwYUJ69OjREcMAAABAw7T7DPNdd92VX/3qV0mS+fPn57LLLss//dM/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\n",
            "text/plain": [
              "<Figure size 1200x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "TLrv8sQZnnP9",
        "colab_type": "code",
        "outputId": "ab49adb7-7849-4f90-885b-1d29f646382e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "time = [\"0h00\",\"1h00\",\"2h00\",\"3h00\",\"4h00\",\"05h00\",\"6h00\",\"7h00\",\"8h00\",\n",
        "       \"9h00\",\"10h00\",\"11h00\",\"12h00\",\"13h00\",\"14h00\",\"15h00\",\"16h00\",\n",
        "       \"17h00\",\"18h00\",\"19h00\",\"20h00\",\"21h00\",\"22h00\",\"23h00\"]\n",
        "plt.bar(df.groupby([pd.to_datetime(df.loc[df['fcojimenez21'] == True].creado_en).dt.hour]).groups.keys(), height=df.groupby([pd.to_datetime(df.loc[df['fcojimenez21'] == True].creado_en).dt.hour]).size())\n",
        "plt.title('@fcojimenez21/Hora')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TxgAAABRNGAMAAFA0YQwAAEDRhDEAAABFE8YAAAAUTRgDAABQNGEMAABA0YQxAAAARRPG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\nAFA0YQwAAEDR/g/mOhQX1wo62gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1200x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jq5xdLvwnxUN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### Preddiction model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A5xmBqoZ2bvx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# remove username\n",
        "df = pd.read_csv(path+\"classification.csv\")\n",
        "df[\"tweet\"] = df[\"tweet\"].str.replace(r\"(@)[A-Za-z0-9_]+\", \"\")\n",
        "df[\"tweet\"] = df[\"tweet\"].dropna()\n",
        "df = df[df[\"tweet\"]!=\"\"]\n",
        "df = df[df[\"PON\"]!=\"\"]\n",
        "df = df[df[\"PON\"]!=\"O\"]\n",
        "\n",
        "df = df[~df[\"PON\"].isna()]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Wx5lWbgx3Fny",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df.to_csv(path+\"ForModel.csv\", encoding ='utf-8')\n",
        "df = pd.read_csv(path+\"ForModel.csv\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ovX6-KME5i-U",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from fastai.text import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "u8rj-PaZ5l0Y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_lm = (TextList.from_csv(path, path+\"tweets.csv\", cols='tweet').split_by_rand_pct(0.1).label_for_lm().databunch())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yCMJLmaT_flP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_lm.save('data_lm.pkl')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9XoLP2vF_h4v",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_lm = load_data(path, 'data_lm.pkl')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2otviTs7BhkU",
        "colab_type": "code",
        "outputId": "9f85ce42-6e84-431b-e912-01ce5ba2abcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "data_lm.show_batch()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>idx</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>xxmaj bien por xxmaj la xxmaj perla del xxmaj pacifico . \\r \\n  y esto xxup no es gracias a # xxmaj matraca @ja … xxbos xxup rt @cendemocratico : .@jimmyjairala inicia en estos momentos su caminata por xxmaj mucho xxmaj lote , al norte de la ciudad . xxmaj lo acompañan @diegospotorno , … xxbos xxup rt @ecuagallos : ¿ xxmaj sabían xxmaj uds . q xxup joba</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>xxmaj hasta que devuelvan el dinero o se realice el concierto ! xxmaj pésima logística en época invernal ! # luismiguel / buena @joseluis_rr 👏 🏼 \\r \\n \\r \\n  # xxunk \\r \\n  # xxunk 🤷 🏼‍ ♂ ️ 🤷 🏼‍ ♂ ️ 🤷 🏼‍ ♂ ️ 🤷 🏼‍ ♂ ️ 🤷 🏼‍ ♂ ️ \\r \\n  # xxunk https : / / t.co / xxunk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>la alcaldía de # xxmaj guayaquil : @jimmyjairala , @fcojimenez21 , … xxbos @mariaotras @elconsiglieri1 @justiceveraec @gpuuiogyedur @aqua2650 @ronpeper @manaranjodiaz @jorgevelezvelez @patrici02306665 @atrixiii @avonbuchwalds @taniaguaranda @fmaritza @albacedeozaval1 @patidblacio @cuenteromuisne @enriquemenoscal @gabriellaperu @dama @cynthiaviteri6 @lulagaray @paolajaramilloz @yatenemospresi @narcymaya2 xxmaj se va se va se va a xxunk en el xxunk de olmedo xxbos xxup rt @cendemocratico : xxmaj chongón y xxmaj puerto xxmaj hondo ya cumplen con las condiciones para</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>candidato a la alcaldía de # xxmaj guay … xxbos xxup rt @cendemocratico : [ # xxup boletín ] @jimmyjairala visitó xxmaj mucho xxmaj lote . xxmaj sus vecinos xxunk que los valores en sus planillas se han xxunk . xxmaj en … xxbos xxunk @jimmyjairala 🤦 ‍ ♂ ️ xxbos xxup rt @cendemocratico : xxmaj vía @canalrtu : @jimmyjairala llegó hasta xxmaj puná , parroquia rural de xxmaj</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>estimados seguidores . xxmaj los invitamos a seguir hoy la entrevista de @jimmyjairala en @radiohuancavilk desde … xxbos xxup rt @damadeallullas : @jennifercoloma xxunk xxunk xxunk xxunk @franksamaniegom xxunk xxunk … xxbos xxup rt @ecuadortv : xxup hoy | xxup 22h00 \\r \\n  xxmaj en # decisiónendemocracia conoceremos a los candidatos a la alcaldía de # xxmaj guayaquil : @jimmyjairala , @fcojimenez21 , … xxbos xxup rt @damadeallullas :</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gsyabWjpB0F7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "weights_pretrained = path+'converted_model'\n",
        "itos_pretrained = path+'itos'\n",
        "pretained_data = (weights_pretrained, itos_pretrained)\n",
        "learn = language_model_learner(data_lm, AWD_LSTM, pretrained_fnames=pretained_data, drop_mult=0.6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xzC_qvGDBqTS",
        "colab_type": "code",
        "outputId": "1587617c-c517-4bae-cfc8-c4fb927de4ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "14RB4UV5CNOV",
        "colab_type": "code",
        "outputId": "3edcad5f-04c0-450f-9934-6fcf6a541998",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot(skip_end=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "p7NNDhYlCOQT",
        "colab_type": "code",
        "outputId": "b4ccf4d0-ad90-4074-f01a-2fb863589b2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(1, 1e-02, moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 00:14 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>5.099202</td>\n",
              "      <td>3.970043</td>\n",
              "      <td>0.314598</td>\n",
              "      <td>00:14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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    {
      "metadata": {
        "id": "oMBglyEfGSoE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('fit_head')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zo0uujMLEaXE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('fit_head');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XeQH0o7XGU1J",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.unfreeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ksPhFK9CFwki",
        "colab_type": "code",
        "outputId": "d80cbeea-0cbc-4b11-af7c-cf0fa7a27d79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(10, 1e-03, moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 03:03 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>3.937126</td>\n",
              "      <td>3.578753</td>\n",
              "      <td>0.354821</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.579814</td>\n",
              "      <td>3.021521</td>\n",
              "      <td>0.446741</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.168866</td>\n",
              "      <td>2.618203</td>\n",
              "      <td>0.509643</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.799575</td>\n",
              "      <td>2.372685</td>\n",
              "      <td>0.555268</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.515592</td>\n",
              "      <td>2.227562</td>\n",
              "      <td>0.578438</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.303267</td>\n",
              "      <td>2.134162</td>\n",
              "      <td>0.594196</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.143560</td>\n",
              "      <td>2.081303</td>\n",
              "      <td>0.602679</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.019376</td>\n",
              "      <td>2.051470</td>\n",
              "      <td>0.607232</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.956064</td>\n",
              "      <td>2.042516</td>\n",
              "      <td>0.609821</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.929175</td>\n",
              "      <td>2.042511</td>\n",
              "      <td>0.609688</td>\n",
              "      <td>00:18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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          },
          "metadata": {
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      ]
    },
    {
      "metadata": {
        "id": "bgi1318qGeTS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('fine_tuned')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G_Fitt-XGgxH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('fine_tuned');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ydb94NuKEBMy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save_encoder('fine_tuned_enc')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5bzC8wqHHWZc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "TEXT = \"La ciudad de Guayaquil\"\n",
        "N_WORDS = 20\n",
        "N_SENTENCES = 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mokOZvPvHXjq",
        "colab_type": "code",
        "outputId": "040a1d77-1e5f-4a43-c56c-2c61802d568e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"\\n\".join(learn.predict(TEXT, N_WORDS, temperature=0.75) for _ in range(N_SENTENCES)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "La ciudad de Guayaquil también se viste de naranja y sueña con # elmejorguayaquildetodoslostiempos con @jimmyjairala y el equipo de @la6mdg xxbos RT\n",
            "La ciudad de Guayaquil se salvó del Socialismo de SXXI . Fue el bastión que nunca se pudieron tomar .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RCNMPwzfH3Dp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_clas = (TextList.from_csv(path, path+\"ForModel.csv\", cols='tweet', vocab=data_lm.vocab).split_by_rand_pct().label_from_df(cols='PON')\n",
        "             .databunch())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YO4C52pD62Wk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_clas.save('data_clas.pkl')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YOpg6llZDoiK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.2)\n",
        "learn.load_encoder('fine_tuned_enc')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "flzCkntTDs2u",
        "colab_type": "code",
        "outputId": "b26a7d1d-f5b3-4159-ddcd-2c8d76e8a80a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "L6A2GmyoEG4C",
        "colab_type": "code",
        "outputId": "08ae10d4-76bb-4787-a2d3-c16d42db8fa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DzqP87LlVHCwoiXZYxoSNJRBjPJSWnMC/3zKa6ZP6ceR4GT//2yrW7yiIdljGhIUlEGM8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\nMS1iCcQYY0yLWAIxxhjTIv8P2dLoWh1bTA4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "B1bmQVPcEIfl",
        "colab_type": "code",
        "outputId": "3d84416f-830c-418d-e58f-260d38227f94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(1, 1e-02, moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 00:01 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.581488</td>\n",
              "      <td>0.656811</td>\n",
              "      <td>0.688742</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Tz7AzCLdEPxi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('first')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qcAU944xFPMx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('first');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gt8ct08gFQ8l",
        "colab_type": "code",
        "outputId": "58a39041-11a5-4598-9878-8b497607b6b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "cell_type": "code",
      "source": [
        "learn.freeze_to(-2)\n",
        "learn.fit_one_cycle(1, slice(1e-02/(2.6**4),1e-02), moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 00:01 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.482573</td>\n",
              "      <td>0.521403</td>\n",
              "      <td>0.807947</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8slywWXPFXd4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('second')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UJyk81CcFbO_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('second');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YSPSVHkjFcl3",
        "colab_type": "code",
        "outputId": "2cd448b4-01c3-4fa3-ffff-ce2a878c95f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "cell_type": "code",
      "source": [
        "learn.freeze_to(-3)\n",
        "learn.fit_one_cycle(1, slice(1e-03/(2.6**4),1e-03), moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 00:02 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.376679</td>\n",
              "      <td>0.479074</td>\n",
              "      <td>0.847682</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4929ATcrFilY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('third')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "prJfSMJcM_XK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('third');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2QSVmNwMNBgr",
        "colab_type": "code",
        "outputId": "065194a1-387c-42dc-cb25-f7825a8ef082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "cell_type": "code",
      "source": [
        "learn.unfreeze()\n",
        "learn.fit_one_cycle(2, slice(1e-03/(2.6**4),1e-03), moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 00:06 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.355888</td>\n",
              "      <td>0.449902</td>\n",
              "      <td>0.834437</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.337926</td>\n",
              "      <td>0.415722</td>\n",
              "      <td>0.854305</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QNIQGnmSOq8W",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('lastModel')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fix1D4TvOyoO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save_encoder('lastModel_enc')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2fpBe-YwO17h",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Algunos ejemplos"
      ]
    },
    {
      "metadata": {
        "id": "u0jBVH1mNE_L",
        "colab_type": "code",
        "outputId": "787a2af2-0b49-4519-df7c-c0cfd1a72fd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.predict(\"Vamos a ganar mi alcalde\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Category P, tensor(1), tensor([0.0649, 0.9351]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 447
        }
      ]
    },
    {
      "metadata": {
        "id": "Pj0ltFItNKmK",
        "colab_type": "code",
        "outputId": "e83d15e1-6113-45fc-cdfa-f3222538cebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.predict(\"Los mismos ladrones de siempre\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Category N, tensor(0), tensor([0.7378, 0.2622]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 448
        }
      ]
    },
    {
      "metadata": {
        "id": "ciK7yFmDOEcW",
        "colab_type": "code",
        "outputId": "47be2ed9-1c9f-4624-96c3-ada6084727e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.predict(\"🐭🐭\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Category N, tensor(0), tensor([0.6929, 0.3071]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 449
        }
      ]
    },
    {
      "metadata": {
        "id": "bwmrtunCOIr1",
        "colab_type": "code",
        "outputId": "1ca62480-c144-4f6a-9021-c5071732e3b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.predict(\"Es hora de un cambio\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Category P, tensor(1), tensor([0.4567, 0.5433]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 450
        }
      ]
    },
    {
      "metadata": {
        "id": "DQvc_JzIOLiG",
        "colab_type": "code",
        "outputId": "df46b5ce-3fef-40d8-d230-57138212db80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.predict(\"Sinico, ladrón\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Category N, tensor(0), tensor([0.7175, 0.2825]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 451
        }
      ]
    },
    {
      "metadata": {
        "id": "0m7wX858OSwV",
        "colab_type": "code",
        "outputId": "36052133-952c-4981-a4a5-5594573f4b5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.predict(\"Tenemos el optimismo y convicción que el pueblo respaldará masivamente la propuesta de \")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Category P, tensor(1), tensor([0.0348, 0.9652]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 452
        }
      ]
    },
    {
      "metadata": {
        "id": "DM_3iXJQObh1",
        "colab_type": "code",
        "outputId": "21ba6436-2750-4d0d-910b-48bd75c39df1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.predict(\"no invierten nada . Porque si no les resulta el fraude y ganar las alcaldías que deseen en especial\")[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category N"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 463
        }
      ]
    },
    {
      "metadata": {
        "id": "LLanul4BOmdd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dff = pd.read_csv(path+\"tweets 21.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e8J3HpCAPrAN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def prediction(x):\n",
        "  return learn.predict(x)[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JgrfAX9DPW7w",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dff[\"prediction\"] = dff[\"tweet\"].apply(prediction)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "azTjam-JPZQw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dff.to_csv(path+\"Final.csv\", encoding ='utf-8')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rX46iov9QaL4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dff = dff[~dff['tweet'].astype(str).str.startswith('RT')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MNShnhukUpnI",
        "colab_type": "code",
        "outputId": "0b6a02de-deb8-450b-ecc9-c0df03dbff86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "print(dff.shape)\n",
        "print(dff.columns)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5298, 31)\n",
            "Index(['ID', 'creado_en', 'nombreusuario', 'nombre', 'creacion_usuario',\n",
            "       'seguidores', 'siguiendo', 'numero_tweets',\n",
            "       'geolocalziacion_habilitada', 'idioma', 'origen', 'idioma_del_tweet',\n",
            "       'tweet', 'hashtags', 'es_respuesta', 'jimmyjairala', 'cedeno_marth',\n",
            "       'jorgevillacrese', 'CynthiaViteri6', 'GinoCornejoM', 'BermudezBronson',\n",
            "       'balerioestacio', 'inca_jose', 'EduardoArgudoG', 'fcojimenez21',\n",
            "       'patobuendia23', 'jaimelomas74', 'Simon_BolivarEC', 'edgarsalazar_51',\n",
            "       'jaimenebotsaadi', 'prediction'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EflLAkI4Wvpk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "jaimenebotsaadi_sent = dff.loc[dff[\"jaimenebotsaadi\"]==True].groupby(\"prediction\").size()\n",
        "jimmyjairala_sent = dff.loc[dff[\"jimmyjairala\"]==True].groupby(\"prediction\").size()\n",
        "CynthiaViteri6_sent = dff.loc[dff[\"CynthiaViteri6\"]==True].groupby(\"prediction\").size()\n",
        "list_pos = [jaimenebotsaadi_sent[1], jimmyjairala_sent[1], CynthiaViteri6_sent[1]]\n",
        "list_neg = [jaimenebotsaadi_sent[0], jimmyjairala_sent[0], CynthiaViteri6_sent[0]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TY2tF4yMZ-dB",
        "colab_type": "code",
        "outputId": "c0af0549-9910-4b9e-f811-bef5ce931277",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "cell_type": "code",
      "source": [
        "N = 3\n",
        "ind = np.arange(N)  # the x locations for the groups\n",
        "width = 0.27 \n",
        "ax = plt.subplot()\n",
        "ax.set_ylabel('Tweets')\n",
        "ax.set_xticks(ind+width)\n",
        "ax.set_xticklabels((\"@jaimenebotsaadi\", \"@jimmyjairala\", \"@CynthiaViteri\") )\n",
        "ax.bar(ind+width, list_pos,width=width,color='g',align='center')\n",
        "ax.bar(ind+width*2, list_neg,width=width,color='r',align='center')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "letFm5Yna-wo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}