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https://git.roussel.pro/telecom-paris/pact.git
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Delete TAL__3_.ipynb
This commit is contained in:
Yannis Grivaux
@@ -1,412 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"#pip install unidecode\n",
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"#pip install dataclass\n",
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"#pip install nltk\n",
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"#import os\n",
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"#from unidecode import unidecode\n",
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"#import nltk\n",
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"#from dataclasses import dataclass"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Première partie : presentation du problème et du materiel\n",
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"\n",
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"Nous cherchons a attribuer à une liste d'avis laissés un score global de satisfaction, ainsi qu'un score de satisfaction concernant chaque point pour lequel il sera particulierement interessant de se pencher (par exemple le delais d'attente dans un parc d'attraction ou la propreté dans un hotel).\n",
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"\n",
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"Nous allons pour cela utiliser une base de mots français associés chacun a un score de positivité, ainsi qu'une liste d'avis concernant le musée du Louvre.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Emplacmement du fichier contenant des mots francais associés a une score sous la forme\n",
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"#mot1->son score\n",
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"#mot2->son score\n",
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"#mot3->son score ...\n",
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"\n",
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"lexiconPath = r\"fr_lexicon.txt\" \n",
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"nomsCommunsPath= r\"mots-communs.txt\"\n",
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"\n",
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"\n",
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"#Emplacmement du fichier contenant des des avis sur le musée du Louvre sous la forme\n",
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"#Avis1\n",
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"#//Avis2\n",
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"#//Avis3 ...\n",
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"\n",
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"reviewPath = r\"LouvreAvis.txt\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Nous créons une liste de listes ordonnée alphabétiquement pour ne pas avoir à chercher un mot d'un avis dans le lexique en entier à chaque fois. La dernière case correspond aux expressions n'étant pas des mots."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"scoreWords = open(lexiconPath, \"r\")\n",
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"scoreTable = [[] for i in range(27)]\n",
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"line = scoreWords.readline()\n",
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"\n",
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"#Fonction d'ajout d'une paire mot-score par ordre alphabétique avec les\n",
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"#expressions n'étant pas des mots à la dernière case.\n",
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"#L'indice de la bonne case est trouvée avec le code ASCII en minuscule\n",
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"#(a vaut 97 et z vaut 122)\n",
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"\n",
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"def add(scoreword):\n",
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" if (ord(scoreword[0][0]) < 97 or ord(scoreword[0][0]) > 122):\n",
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" scoreTable[26].append(scoreword)\n",
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" else:\n",
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" scoreTable[ord(scoreword[0][0])-97].append(scoreword)\n",
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" \n",
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"#Ajout des paires mot-score dans scoreTable\n",
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"while (line != ''):\n",
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" line = line.strip().split(\"->\")\n",
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" add([line[0].lower(), float(line[1])])\n",
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" line = scoreWords.readline()\n",
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"scoreWords.close()\n",
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"\n",
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"print(line)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['abandon', -2.4]\n",
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"['abandonnant', -1.6]\n",
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"['abandonne', -1.3]\n",
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"['badass', 1.4]\n",
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"['badin', 1.2]\n",
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"['badine', 1.2]\n",
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"['cachant', -1.2]\n",
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"['cache', -0.7]\n",
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"['cachent', -0.7]\n"
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]
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}
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],
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"source": [
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"for i in range (3):\n",
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" print(scoreTable[i][0])\n",
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" print(scoreTable[i][1])\n",
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" print(scoreTable[i][2])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Deuxieme partue : analyse d'avis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\yanni\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
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" warnings.warn(msg, category=FutureWarning)\n"
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]
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}
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],
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"source": [
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"file = open(reviewPath, \"r\", encoding='UTF-8')\n",
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"reviews = (file.read()).split('//')\n",
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" \n",
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"import pandas as pd\n",
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"from sklearn.feature_extraction.text import *\n",
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"dataset = reviews\n",
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"\n",
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"mots_communs=[\"\"]\n",
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" \n",
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"tfIdfVectorizer=TfidfVectorizer(use_idf=True)\n",
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"tfIdf = tfIdfVectorizer.fit_transform(dataset)\n",
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"df = pd.DataFrame(tfIdf[0].T.todense(), index=tfIdfVectorizer.get_feature_names(), columns=[\"TF-IDF\"])\n",
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"df = df.sort_values('TF-IDF', ascending=False)\n",
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"liste = df.head(50)\n",
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"listeMotsTFIDF=list(liste.index)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" \n",
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" mots dans mots communs \n",
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" \n",
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" ['homme', 'le', 'de', 'est', 'pas', 'peu', 'un', 'une', 'la', 'des', 'mari', 'femme', 'jour', 'mer', 'temps', 'main', 'chose', 'vie', 'yeux', 'heure', 'enfant', 'fois', 'moment', 'tête', 'père', 'fille', 'coeur', 'an', 'terre', 'dieu', 'monsieur', 'voix', 'maison', 'coup', 'air', 'mot', 'ami', 'porte', 'amour', 'pied', 'pays', 'ciel', 'frère', 'regard', 'âme', 'côté', 'ville', 'rue', 'soir', 'chambre', 'pas', 'soleil', 'roi', 'état', 'corps', 'bras', 'parti', 'année', 'visage', 'lettre', 'franc', 'fond', 'force', 'effet', 'saint', 'idée', 'mois', 'fils', 'raison', 'point', 'personne', 'peuple', 'fait', 'parole', 'guerre', 'pensée', 'affaire', 'matin', 'pierre', 'doute', 'front', 'ombre', 'part', 'maître', 'besoin', 'question', 'peine', 'madame', 'sorte', 'figure', 'droit', 'bout', 'bois', 'mari', 'feu', 'partie', 'face', 'mouvement', 'arbre', 'cas', 'mur', 'ordre', 'est', 'travers', 'instant', 'façon', 'oeil', 'forme', 'cheveu', 'suite', 'être', 'nature', 'or', 'pouvoir', 'bouche', 'sens', 'cri', 'espèce', 'cheval', 'loi', 'ministre', 'société', 'politique', 'oreille', 'fortune', 'compte', 'manier', 'action', 'garçon', 'exemple', 'premier', 'projet', 'étude', 'journal', 'geste', 'situation', 'oiseau', 'siècle', 'million', 'groupe', 'centre', 'chien', 'peau', 'reste', 'nombre', 'mesure', 'article', 'vue', 'âge', 'système', 'rêve', 'rapport', 'soldat', 'lèvre', 'signe', 'vérité', 'dos', 'dame', 'doigt', 'objet', 'fer', 'lendemain', 'train', 'papa', 'secret', 'haut', 'vieillard', 'docteur', 'ton', 'jambe', 'minute', 'nuage', 'présence', 'épaule', 'feuille', 'résultat', 'hôtel', 'semaine', 'forêt', 'qualité', 'prince', 'bien', 'médecin', 'volonté', 'seigneur', 'ligne', 'condition', 'classe', 'voyage', 'présent', 'CommonWords1.txt', 'tout', 'même', 'autre', 'seul', 'jeune', 'premier', 'quel', 'dernier', 'blanc', 'vrai', 'toute', 'rouge', 'humain', 'général', 'français', 'politique', 'bleu', 'social', 'certain', 'différent', 'ne', 'pas', 'si', 'là', 'même', 'tout', 'encore', 'aussi', 'alors', 'non', 'très', 'ainsi', 'ici', 'oui', 'déjà', 'tant', 'enfin', 'maintenant', 'point', 'presque', 'ailleurs', \"aujourd'hui\", 'autour', 'dessus', 'comme', 'comment', 'autant', \"d'abord\", 'surtout', 'cependant', 'pourtant', 'ci', 'vraiment', 'bientôt', 'partout', 'debout', 'plutôt', 'combien', 'hier', 'parfois', 'et', 'que', 'comme', 'mais', 'ou', 'quand', 'si', 'puis', 'donc', 'car', 'ni', 'parce que', 'pourquoi', 'lorsque', 'tandis que', 'puisque', 'comment', 'soit', 'or', 'le', 'un', 'son', 'ce', 'du', 'au', 'de', 'mon', 'leur', 'notre', 'votre', 'quelque', 'ton', 'tout', 'chaque', 'aucun', 'tel', 'certain', 'plusieurs', \"d'autres\", 'deux', 'cent', 'mille', 'trois', 'quatre', 'vingt', 'cinq', 'dix', 'neuf', 'six', 'huit', 'sept', 'trente', 'quarante', 'cinquante', 'quinze', 'douze', 'un', 'à', 'en', 'dans', 'pour', 'par', 'sur', 'avec', 'sans', 'sous', 'après', 'entre', 'vers', 'chez', 'jusque', 'contre', 'devant', 'depuis', 'pendant', 'avant', 'voilà', 'près', 'dès', 'malgré', 'voici', 'selon', 'derrière', 'parmi', 'afin de', 'auprès', 'quant à', 'hors', 'durant', 'grâce', 'il', 'je', 'se', 'qui', 'elle', 'ce', 'le', 'que', 'vous', 'me', 'on', 'lui', 'nous', 'y', 'en', 'où', 'tu', 'moi', 'te', 'celui', 'dont', 'tout', 'ça', 'cela', 'autre', 'un', 'toi', 'lequel', 'leur', 'quoi', \"l'un\", 'chacun', 'auquel', \"quelqu'un\", \"d'autres\", 'ceci', \"l'une\", 'soi', 'sien', 'mien', 'aucu']\n",
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"\n",
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" \n",
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" mots dans khey de base \n",
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" \n",
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" ['attente', \"d'attente\", 'queue', 'patienter', 'patience', 'patient', 'patients', 'patiente', 'patientes', 'impolitesse', 'impolie', 'impolies', 'impoli', 'impolis', 'gentillesse', 'amabilité', 'aimable', 'aimables', 'gentil', 'gentils', 'gentille', 'gentilles', 'personnel', 'sales', 'sale', 'saleté', 'propre', 'propres', 'propreté', 'acceuil', 'prix', 'cher', 'chers', 'chère', 'chères', 'onéreux', 'onéreuse', 'onéreuses', 'abordable', 'raisonnable', 'raisonnables', 'accessible', 'accessibilité', 'orienter', 'employé', 'employés', 'employées', 'employée', 'orientation', 'orienté', \"s'orienter\", 'désorienter', 'désorienté', 'désorientée', 'désorientés', 'désorientées', 'panneau', 'panneaux', 'signalétique', 'labyrinthe', 'perdu', 'perdus', 'perdue', 'perdues']\n",
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"\n",
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" \n",
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" mots dans listeMotsTFIDF sans les mots communs \n",
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" \n",
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" ['regardé', 'venu', 'trouve', 'travaillant', 'carte', 'securitas', 'richelieu', 'responsable', '10h30', 'public', 'escalator', 'grossièrement', 'handicapé', 'paris', 'inadmissible', 'jo', 'niveau', 'lamentable', 'montant', 'rendu', 'accueillis', 'épouse', 'accueil', 'agente', 'accéder', 'arrivée', 'sécurité', 'heureusement', 'avait', 'été', 'avons', 'faire']\n",
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"\n",
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" \n",
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" mots dans keys \n",
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" \n",
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" ['attente', \"d'attente\", 'queue', 'patienter', 'patience', 'patient', 'patients', 'patiente', 'patientes', 'impolitesse', 'impolie', 'impolies', 'impoli', 'impolis', 'gentillesse', 'amabilité', 'aimable', 'aimables', 'gentil', 'gentils', 'gentille', 'gentilles', 'personnel', 'sales', 'sale', 'saleté', 'propre', 'propres', 'propreté', 'acceuil', 'prix', 'cher', 'chers', 'chère', 'chères', 'onéreux', 'onéreuse', 'onéreuses', 'abordable', 'raisonnable', 'raisonnables', 'accessible', 'accessibilité', 'orienter', 'employé', 'employés', 'employées', 'employée', 'orientation', 'orienté', \"s'orienter\", 'désorienter', 'désorienté', 'désorientée', 'désorientés', 'désorientées', 'panneau', 'panneaux', 'signalétique', 'labyrinthe', 'perdu', 'perdus', 'perdue', 'perdues', 'regardé', 'venu', 'trouve', 'travaillant', 'carte', 'securitas', 'richelieu', 'responsable', '10h30', 'public', 'escalator', 'grossièrement', 'handicapé', 'paris', 'inadmissible', 'jo', 'niveau', 'lamentable', 'montant', 'rendu', 'accueillis', 'épouse', 'accueil', 'agente', 'accéder', 'arrivée', 'sécurité', 'heureusement', 'avait', 'été', 'avons', 'faire']\n"
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]
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}
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],
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"source": [
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"#print(listeMotsTFIDF)\n",
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"\n",
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"print(\"\\n \\n mots dans mots communs \\n \\n\", motsCommuns) \n",
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"\n",
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"\n",
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"#liste (partielle) de mots-clé pertinents pour un musée\n",
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"keys=['attente', \"d'attente\", 'queue', 'patienter', 'patience', 'patient',\n",
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" 'patients', 'patiente', 'patientes',\n",
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" 'impolitesse' ,'impolie', 'impolies', 'impoli', 'impolis',\n",
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" 'gentillesse', 'amabilité', 'aimable', 'aimables','gentil', 'gentils',\n",
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" 'gentille', 'gentilles', 'personnel',\n",
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" 'sales', 'sale', 'saleté', 'propre', 'propres', 'propreté',\n",
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" 'acceuil', 'prix', 'cher', 'chers', 'chère', 'chères',\n",
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" 'onéreux', 'onéreuse', 'onéreuses', 'abordable',\n",
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" 'raisonnable', 'raisonnables', 'accessible', 'accessibilité', 'orienter','employé',\n",
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" 'employés', 'employées', 'employée',\n",
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" 'orientation', 'orienté', \"s'orienter\",\n",
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" 'désorienter', 'désorienté', 'désorientée', 'désorientés', 'désorientées',\n",
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" 'panneau', 'panneaux', 'signalétique', 'labyrinthe',\n",
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" 'perdu', 'perdus', 'perdue', 'perdues']\n",
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"\n",
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"print(\"\\n \\n mots dans khey de base \\n \\n\", keys) \n",
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"\n",
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"\n",
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"file = open(nomsCommunsPath, \"r\")\n",
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"motsCommuns = file.readlines()\n",
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"file.close()\n",
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"\n",
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"for i in range (len(motsCommuns)) :\n",
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" motsCommuns[i] = motsCommuns[i][:-1]\n",
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" \n",
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"for mot in listeMotsTFIDF :\n",
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" if mot in motsCommuns :\n",
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" listeMotsTFIDF=[i for i in listeMotsTFIDF if i!=mot]\n",
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"\n",
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"for mot in listeMotsTFIDF :\n",
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" if not mot in keys :\n",
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" keys.append(mot)\n",
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"print(\"\\n \\n mots dans listeMotsTFIDF sans les mots communs \\n \\n\", listeMotsTFIDF) \n",
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"print(\"\\n \\n mots dans keys \\n \\n\", keys)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Tableau de paires mots-clé, score associé\n",
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"keyWords = []\n",
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"\n",
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"#Score moyen d'un avis\n",
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"averageScore = 0\n",
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"\n",
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"#Fonction de recherche d'un mot d'un avis parmis le lexique\n",
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"def search(word):\n",
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" if (len(word) != 0):\n",
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" if (ord(word[0]) < 97 or ord(word[0]) > 122):\n",
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" mots = list(e[0] for e in scoreTable[26])\n",
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" if (word in mots):\n",
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" return([word, scoreTable[26][mots.index(word)][1]])\n",
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" else:\n",
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" return(-1)\n",
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" mots = list(e[0] for e in scoreTable[ord(word[0])-97])\n",
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" if (word in mots):\n",
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" return([word, scoreTable[ord(word[0])-97][mots.index(word)][1]])\n",
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" return(-1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"for Review in reviews:\n",
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" #print(Review)\n",
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" reviewScore = 0\n",
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" miniKey = []\n",
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" #recherche de mots positifs/négatifs\n",
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" review = list(e.strip(',.') for e in Review.split())\n",
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" for Word in review:\n",
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" word = Word.lower()\n",
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" temp = search(word)\n",
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" #recherche d'un éventuel mot-clé associé à ce caractère positif/négatif\n",
|
||||
" if (temp != -1):\n",
|
||||
" for key in keys:\n",
|
||||
" if (key in review):\n",
|
||||
" cles = list(e[0] for e in keyWords)\n",
|
||||
" if (key in cles):\n",
|
||||
" keyWords[cles.index(key)][1] += temp[1]\n",
|
||||
" else:\n",
|
||||
" keyWords.append([key, temp[1]])\n",
|
||||
" miniKey.append(key)\n",
|
||||
" reviewScore += temp[1]\n",
|
||||
" averageScore += reviewScore\n",
|
||||
" #Caractéristique de l'avis analysé\n",
|
||||
" miniKey = set(miniKey)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Troisiere partie : affichage des resultats"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Format: [[Mot-clé, score associé]]\n",
|
||||
"[['regardé', -0.7999999999999998], ['venu', -0.7999999999999998], ['trouve', -0.7999999999999998], ['carte', -0.7999999999999998], ['responsable', -0.7999999999999998], ['10h30', -0.7999999999999998], ['public', -0.7999999999999998], ['escalator', -0.7999999999999998], ['grossièrement', -0.7999999999999998], ['handicapé', -0.7999999999999998], ['inadmissible', -0.7999999999999998], ['niveau', -0.7999999999999998], ['lamentable', -0.7999999999999998], ['montant', -0.7999999999999998], ['rendu', -0.7999999999999998], ['accueillis', -0.7999999999999998], ['épouse', -0.7999999999999998], ['agente', -0.7999999999999998], ['accéder', -0.8999999999999999], ['sécurité', 2.4], ['heureusement', -0.7999999999999998], ['avait', 6.6], ['été', -0.8999999999999999], ['avons', 4.800000000000001], ['faire', 5.300000000000001], ['cher', -0.5000000000000002], ['orientation', -0.5000000000000002], ['abordable', 2.4000000000000004], ['personnel', 2.1999999999999997], [\"s'orienter\", 4.5], ['chères', -2.4], ['queue', -0.09999999999999876], ['orienter', -0.30000000000000004], ['prix', 7.4], ['raisonnable', 7.4], [\"d'attente\", 0.9000000000000012]]\n",
|
||||
"Nombre d'avis: 23\n",
|
||||
"Score moyen d'un avis: 1.6478260869565218\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"averageScore /= len(reviews)\n",
|
||||
"print(\"Format: [[Mot-clé, score associé]]\")\n",
|
||||
"print(keyWords)\n",
|
||||
"print(\"Nombre d'avis: \", len(reviews))\n",
|
||||
"print(\"Score moyen d'un avis: \", averageScore)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in New Issue
Block a user