nlp_a2019_tp3/commentaires.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import parsing_functions as pf\n",
    "import re\n",
    "import pandas as pd\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "listOfFiles = pf.getListOfFiles(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires = []\n",
    "\n",
    "for xlpath in listOfFiles:\n",
    "    comments_df = []\n",
    "    media, post_id = re.match(r\"data/([A-Z]+)/comments([0-9a-z\\-]+)\\.xlsx\",xlpath).groups()\n",
    "    comments_df = pf.get_comments(xlpath)\n",
    "    comments_df['media']=media\n",
    "    comments_df['post_id']=post_id\n",
    "    commentaires.append(comments_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df = pd.concat(commentaires, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentaires_df.to_csv(\"refined_data/commentaires_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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