nlp_a2019_tp3/Traitement Articles.ipynb

{
 "cells": [
  {
   "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 toktok, sent_tokenize\n",
    "from nltk.parse import CoreNLPParser\n",
    "import re\n",
    "import pickle\n",
    "import emoji\n",
    "import pretraitement as pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tok = toktok.ToktokTokenizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='pos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='ner')\n",
    "#https://github.com/nltk/nltk/wiki/Stanford-CoreNLP-API-in-NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "textes_articles_df = pd.read_csv(\"refined_data/textes_articles_df.csv\")\n",
    "textes_articles_df = textes_articles_df[textes_articles_df[\"text\"].notnull() & (textes_articles_df[\"media\"]!='CNN')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "del textes_articles_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "article_pretraite = [pr.pretraitement(x,tok,ner_tagger,pos_tagger) for x in list(textes_articles_df[\"text\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "textes_articles_df['ner_dict']=[pr.aggreger_ner_tags(article) for article in article_pretraite]\n",
    "textes_articles_df['pos_dict']=[pr.aggreger_pos_tags(article) for article in article_pretraite]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"pickle/textes_articles_df.pickle\",\"wb\")\n",
    "pickle.dump(textes_articles_df,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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