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https://git.roussel.pro/telecom-paris/pact.git
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intégration de la détéction audio à l'interface
This commit is contained in:
Quentin Roussel
268
code_autre/Traitement Langage Naturel/TAL.ipynb
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268
code_autre/Traitement Langage Naturel/TAL.ipynb
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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": 62,
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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": 63,
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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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"\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": 82,
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"metadata": {},
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"outputs": [],
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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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]
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},
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{
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"cell_type": "code",
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"execution_count": 83,
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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": "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": 67,
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"metadata": {},
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"outputs": [],
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"source": [
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"file = open(reviewPath, \"r\")\n",
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"reviews = (file.read()).split('//')\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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"#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": 68,
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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",
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" if (temp != -1):\n",
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" for key in keys:\n",
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" if (key in review):\n",
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" cles = list(e[0] for e in keyWords)\n",
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" if (key in cles):\n",
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" keyWords[cles.index(key)][1] += temp[1]\n",
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" else:\n",
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" keyWords.append([key, temp[1]])\n",
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" miniKey.append(key)\n",
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" reviewScore += temp[1]\n",
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" averageScore += reviewScore\n",
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" #Caractéristique de l'avis analysé\n",
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" miniKey = set(miniKey)\n"
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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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"# Troisiere partie : affichage des resultats"
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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": 69,
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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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"Format: [[Mot-clé, score associé]]\n",
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"[['cher', -2.7], ['orientation', -2.7], ['abordable', 3.4000000000000004], ['personnel', -3.0999999999999996], [\"s'orienter\", -0.10000000000000053], ['queue', 1.0000000000000004], ['orienter', -0.30000000000000004], ['prix', 7.4], ['raisonnable', 7.4], [\"d'attente\", 0.9000000000000012]]\n",
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"Nombre d'avis: 23\n",
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"Score moyen d'un avis: 1.5652173913043481\n"
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]
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}
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],
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"source": [
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"averageScore /= len(reviews)\n",
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"print(\"Format: [[Mot-clé, score associé]]\")\n",
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"print(keyWords)\n",
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"print(\"Nombre d'avis: \", len(reviews))\n",
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"print(\"Score moyen d'un avis: \", averageScore)"
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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": 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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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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