{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aller chercher les synsets\n",
    "# Variantes morphologiques\n",
    "# Enlever les noms des autres commenteux\n",
    "# Traiter les émoticones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.parse import CoreNLPParser\n",
    "import re\n",
    "import pickle\n",
    "import emoji\n",
    "import pretraitement as pr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenisation des commentaires\n",
    "\n",
    "Utilisation du TweetTokenizer, car il est davantage adapté au contenu des utilisateurs sur les médias sociaux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Création de l'objet Tokenizer\n",
    "tok = TweetTokenizer(preserve_case=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='pos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='ner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df = pd.read_csv(\"refined_data/commentaires_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#suppression de la première colonne qui ne sert à rien\n",
    "del commentaires_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Noms des auteurs\n",
    "\n",
    "Extraction du nom des auteurs pour chaque commentaire et ses sous-commentaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_df = pd.DataFrame(commentaires_df.groupby(['post_id','comment_id'])['name'], columns=['post_comment','list_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_df['list_names'] = names_df.apply(lambda x: list(set(x['list_names'])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_df['post_id'] = names_df.apply(lambda x: x['post_comment'][0], axis=1)\n",
    "names_df['comment_id'] = names_df.apply(lambda x: x['post_comment'][1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del names_df['post_comment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traitement du nom des auteurs dans les textes des commentaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df_names = commentaires_df.merge(names_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_auteurs_referes(comment,names):\n",
    "    auteurs_referes = []\n",
    "    try:\n",
    "        if len(names) > 0:\n",
    "            for i in range(len(names)):\n",
    "                if (comment.find(names[i]) >=0):\n",
    "                    auteurs_referes.append(names[i])\n",
    "        return list(set(auteurs_referes))\n",
    "    except:\n",
    "        return auteurs_referes\n",
    "\n",
    "def remove_names(comment,names):\n",
    "    try:\n",
    "        if len(names) > 0:\n",
    "            for i in range(len(names)):\n",
    "                comment = comment.replace(names[i],'')\n",
    "        return comment\n",
    "    except:\n",
    "        return comment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nettoyage des commentaires et traitement des émoticones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df_names['auteurs_referes'] = commentaires_df_names.apply(lambda x: str(list_auteurs_referes(x['comment'],x['list_names'])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df_names['comment_clean'] = commentaires_df_names.apply(lambda x: str(remove_names(x['comment'],x['list_names'])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df_names.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_pretraite = []\n",
    "compteur=0\n",
    "for x in list(commentaires_df_names[\"comment_clean\"]):\n",
    "    print(str(compteur)+\": \"+x)\n",
    "    commentaires_pretraite.append(pr.pretraitement(x,tok,ner_tagger,pos_tagger))\n",
    "    compteur += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_pretraite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df_names['ner_dict']=[pr.aggreger_ner_tags(article) for article in commentaires_pretraite]\n",
    "commentaires_df_names['pos_dict']=[pr.aggreger_pos_tags(article) for article in commentaires_pretraite]\n",
    "commentaires_df_names['emoji_dict']=[pr.aggreger_emoji(article) for article in commentaires_pretraite]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"pickle/commentaires_df.pickle\",\"wb\")\n",
    "pickle.dump(commentaires_df_names,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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