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François Pelletier 2019-12-15 20:31:38 -05:00
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.gitignore vendored
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data/
pickle/
refined_data/
# ---> Python
# Byte-compiled / optimized / DLL files
__pycache__/

261
Commentaires sur les réseaux sociaux.mm Normal file
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<node TEXT="Signes qui ne vont pas ensemble, dans un m&#xea;me discours. Pour marquer l&apos;opposition implicite" ID="ID_1864195644" CREATED="1576434347915" MODIFIED="1576434379898"/>
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<node TEXT="Saisons, f&#xea;tes" ID="ID_566591651" CREATED="1576437044816" MODIFIED="1576437049959"/>
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<node TEXT="&#xc9;ducation et expertise" FOLDED="true" ID="ID_869848700" CREATED="1576437570827" MODIFIED="1576437579348">
<node TEXT="Montrer son expertise" ID="ID_1657747338" CREATED="1576437580284" MODIFIED="1576437611763"/>
<node TEXT="Se r&#xe9;clamer le droit de mener la discussion (entitlement)" ID="ID_1490384821" CREATED="1576437612334" MODIFIED="1576437625926"/>
<node TEXT="Montrer ses r&#xe9;ussites acad&#xe9;miques" ID="ID_1508946328" CREATED="1576437628508" MODIFIED="1576437637956"/>
<node TEXT="R&#xe9;f&#xe9;rences, imp&#xe9;ratifs, pr&#xe9;supposition, souhaits" ID="ID_718153801" CREATED="1576437668961" MODIFIED="1576437681186"/>
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<node TEXT="Engagement" ID="ID_1367600286" CREATED="1576438606159" MODIFIED="1576438609780"/>
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{
"cells": [
{
"cell_type": "code",
"execution_count": 153,
"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"
]
},
{
"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": 19,
"metadata": {},
"outputs": [],
"source": [
"# Prétraitement\n",
"def pretraitement(article):\n",
" # tokeniser par phrases\n",
" article_sentences = sent_tokenize(article)\n",
" article_ner_tokens = []\n",
" article_pos_tokens = []\n",
" compteur_phrase = 0\n",
" for sentence in article_sentences:\n",
" # Tokeniser\n",
" sentence_tokens = tok.tokenize(sentence)\n",
" # Assembler les entités nommées et colocations\n",
" sentence_ner = ner_tagger.tag(sentence_tokens)\n",
" ner_tokens = [ner_token for ner_token in sentence_ner if ner_token[1] != 'O']\n",
" # Supprimer les classes fermées avec un POS\n",
" sentence_pos = pos_tagger.tag(sentence_tokens)\n",
" pos_tokens = [pos_token for pos_token in sentence_pos if pos_token[1] in ['ADJ','ADV','INTJ','NOUN','PROPN','VERB']]\n",
" # Ajouter à la liste de phrases tokenisées\n",
" article_ner_tokens.append(ner_tokens)\n",
" article_pos_tokens.append(pos_tokens)\n",
" compteur_phrase += 1\n",
" return article_ner_tokens, article_pos_tokens"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"article_pretraite = [pretraitement(x) for x in list(textes_articles_df[\"text\"])]"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [],
"source": [
"def aggreger_ner_tags(article):\n",
" dict_named_entity = {}\n",
" for sentence in article[0]:\n",
" for entity in sentence:\n",
" dict_named_entity[entity] = dict_named_entity.get(entity,0) + 1\n",
" return dict_named_entity"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"def aggreger_pos_tags(article):\n",
" dict_pos = {}\n",
" for sentence in article[1]:\n",
" for pos in sentence:\n",
" dict_pos[pos] = dict_pos.get(pos,0) + 1\n",
" return dict_pos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [],
"source": [
"textes_articles_df['ner_dict']=[aggreger_ner_tags(article) for article in article_pretraite]\n",
"textes_articles_df['pos_dict']=[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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

2036
Traitement commentaires.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import parsing_functions as pf\n",
"import re\n",
"import pandas as pd\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"listOfFiles = pf.getListOfFiles(\"data\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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": 4,
"metadata": {},
"outputs": [],
"source": [
"commentaires_df = pd.concat(commentaires, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"commentaires_df.to_csv(\"refined_data/commentaires_df.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
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"nbformat_minor": 4
}

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<node TEXT="Nature du langage" ID="ID_397305792" CREATED="1576432966755" MODIFIED="1576432977141">
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</node>
<node TEXT="R&#xe9;pertoire" ID="ID_1024196052" CREATED="1576433048058" MODIFIED="1576433050691">
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<node TEXT="Contexte psychologique" ID="ID_1939058865" CREATED="1576433112090" MODIFIED="1576433123678"/>
<node TEXT="Contexte sociopolitique" ID="ID_791737082" CREATED="1576433124443" MODIFIED="1576433130862"/>
</node>
</node>
<node TEXT="&#xc9;l&#xe9;ments d&apos;analyse" ID="ID_994837948" CREATED="1576433185944" MODIFIED="1576433207370">
<node TEXT="Intertextualit&#xe9;" ID="ID_1353601895" CREATED="1576433212711" MODIFIED="1576433223175">
<node TEXT="Liens avec les textes pr&#xe9;c&#xe9;dents" ID="ID_261026690" CREATED="1576433278055" MODIFIED="1576433286703"/>
</node>
<node TEXT="Interdiscursivit&#xe9;" ID="ID_1355846398" CREATED="1576433224084" MODIFIED="1576433232077">
<node TEXT="Interaction et superposition des &#xe9;changes" ID="ID_1296550423" CREATED="1576433265025" MODIFIED="1576433274898"/>
</node>
<node TEXT="Multimodalit&#xe9;" ID="ID_398609083" CREATED="1576433232685" MODIFIED="1576433253744">
<node TEXT="Images" ID="ID_996455266" CREATED="1576433241535" MODIFIED="1576433243920"/>
<node TEXT="Textes" ID="ID_1799781603" CREATED="1576433244372" MODIFIED="1576433245825"/>
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</node>
</node>
<node TEXT="Localisation" FOLDED="true" ID="ID_206247466" CREATED="1576433752181" MODIFIED="1576433841871">
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<node TEXT="&#xc9;l&#xe9;ment culturel" ID="ID_726017284" CREATED="1576433850747" MODIFIED="1576433859642"/>
</node>
<node TEXT="M&#xe9;tonymie" FOLDED="true" ID="ID_1450191917" CREATED="1576433862048" MODIFIED="1576433910984">
<node TEXT="Inclus la localisation, mais dans un autre type lexical" ID="ID_1282192414" CREATED="1576433915495" MODIFIED="1576433947584"/>
</node>
<node TEXT="Personnification" ID="ID_1888056104" CREATED="1576433968022" MODIFIED="1576433972282"/>
<node TEXT="S&#xe9;miotique transgressive" ID="ID_508849276" CREATED="1576434327152" MODIFIED="1576434342653">
<node TEXT="Signes qui ne vont pas ensemble, dans un m&#xea;me discours. Pour marquer l&apos;opposition implicite" ID="ID_1864195644" CREATED="1576434347915" MODIFIED="1576434379898"/>
</node>
</node>
<node TEXT="Temps" FOLDED="true" ID="ID_1259089697" CREATED="1576436972135" MODIFIED="1576436974049">
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<node TEXT="Notion de maintenant" ID="ID_63573093" CREATED="1576436979945" MODIFIED="1576436985093"/>
<node TEXT="Cycles" ID="ID_1458339480" CREATED="1576437010439" MODIFIED="1576437014557"/>
<node TEXT="Saisons, f&#xea;tes" ID="ID_566591651" CREATED="1576437044816" MODIFIED="1576437049959"/>
<node TEXT="Pass&#xe9; et futur" ID="ID_1383560062" CREATED="1576437051190" MODIFIED="1576437056318"/>
<node TEXT="&#xc2;ge, anniversaires" ID="ID_1886330627" CREATED="1576437056834" MODIFIED="1576437061864"/>
</node>
<node TEXT="&#xc9;ducation et expertise" FOLDED="true" ID="ID_869848700" CREATED="1576437570827" MODIFIED="1576437579348">
<node TEXT="Montrer son expertise" ID="ID_1657747338" CREATED="1576437580284" MODIFIED="1576437611763"/>
<node TEXT="Se r&#xe9;clamer le droit de mener la discussion (entitlement)" ID="ID_1490384821" CREATED="1576437612334" MODIFIED="1576437625926"/>
<node TEXT="Montrer ses r&#xe9;ussites acad&#xe9;miques" ID="ID_1508946328" CREATED="1576437628508" MODIFIED="1576437637956"/>
<node TEXT="R&#xe9;f&#xe9;rences, imp&#xe9;ratifs, pr&#xe9;supposition, souhaits" ID="ID_718153801" CREATED="1576437668961" MODIFIED="1576437681186"/>
</node>
<node TEXT="Position" ID="ID_727200733" CREATED="1576438549864" MODIFIED="1576438559654">
<node TEXT="Expression" FOLDED="true" ID="ID_986508069" CREATED="1576438633543" MODIFIED="1576438637443">
<node TEXT="Attitude" ID="ID_274391283" CREATED="1576438566378" MODIFIED="1576438593868"/>
<node TEXT="&#xc9;motion" ID="ID_920344361" CREATED="1576438594326" MODIFIED="1576438596613"/>
<node TEXT="Croyance" ID="ID_592625157" CREATED="1576438596770" MODIFIED="1576438599820"/>
<node TEXT="&#xc9;valuation/jugement" ID="ID_717619617" CREATED="1576438600172" MODIFIED="1576438605956"/>
<node TEXT="Engagement" ID="ID_1367600286" CREATED="1576438606159" MODIFIED="1576438609780"/>
</node>
<node TEXT="Attributs linguistiques" ID="ID_825473217" CREATED="1576438657794" MODIFIED="1576438663960">
<node TEXT="Modalit&#xe9;" ID="ID_197540513" CREATED="1576438664912" MODIFIED="1576438672368"/>
<node TEXT="&#xc9;valuation" ID="ID_1335244587" CREATED="1576438672980" MODIFIED="1576438676694"/>
<node TEXT="Politesse" ID="ID_247379549" CREATED="1576438698682" MODIFIED="1576438703989"/>
<node TEXT="&#xc9;videntialit&#xe9;" ID="ID_1818362871" CREATED="1576438704862" MODIFIED="1576438708497"/>
<node TEXT="Intensit&#xe9;" ID="ID_517078263" CREATED="1576438742226" MODIFIED="1576438746807"/>
</node>
</node>
</node>
</node>
</map>

57
parsing_functions.py Normal file
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@ -0,0 +1,57 @@
import os
import re
import pandas as pd
import requests
from urllib.parse import unquote
import newspaper
def get_comments(file_path):
df = pd.read_excel(file_path, skiprows=5,
names=['comment_id',
'nested_id',
'name',
'id',
'date',
'likes',
'comment',
'source'])
df["comment_id"] = df["comment_id"].mask(pd.isnull,df["nested_id"].mask(pd.isnull,"0-0").apply(lambda x: re.match(r"([0-9]+)\-([0-9]+)",x).group(1)))
df["nested_id"] = df["nested_id"].mask(pd.isnull,"0-0").apply(lambda x: re.match(r"([0-9]+)\-([0-9]+)",x).group(2))
del df["source"]
return df
def get_text_article(file_path):
url = pd.read_excel(file_path,skiprows=1,nrows=1,header=None,names=['source','url']+['']*6)['url'][0]
request_url = requests.get(url)
html_content = str(request_url.content)
link_urls = re.findall(r'http[s]?://l\.facebook(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',html_content)
u_link_urls = [unquote(unquote(u.replace("https://l.facebook.com/l.php?u=",""))) for u in link_urls]
article = newspaper.Article('')
try:
url_article = [re.search("(.*)&amp;.*",u).group(1) for u in u_link_urls][-3]
request_url_article = requests.get(url_article)
article.set_html(request_url_article.content)
except:
article.set_html(html_content)
try:
article.parse()
text_article = article.text.replace("\n\n"," ")
except:
text_article = ""
return text_article
def getListOfFiles(dirName):
# create a list of file and sub directories
# names in the given directory
listOfFile = os.listdir(dirName)
allFiles = list()
# Iterate over all the entries
for entry in listOfFile:
# Create full path
fullPath = os.path.join(dirName, entry)
# If entry is a directory then get the list of files in this directory
if os.path.isdir(fullPath):
allFiles = allFiles + getListOfFiles(fullPath)
else:
allFiles.append(fullPath)
return allFiles

113
textes_articles.ipynb Normal file
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@ -0,0 +1,113 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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": [
"textes_articles = []\n",
"for xlpath in listOfFiles:\n",
" time.sleep(3)\n",
" media, post_id = re.match(r\"data/([A-Z]+)/comments([0-9a-z\\-]+)\\.xlsx\",xlpath).groups()\n",
" textes_articles.append([media,post_id,pf.get_text_article(xlpath)])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'textes_articles' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-b88ef33508d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtextes_articles_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtextes_articles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'media'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'post_id'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'textes_articles' is not defined"
]
}
],
"source": [
"textes_articles_df = pd.DataFrame(textes_articles, columns=['media','post_id','text'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'textes_articles_df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-cc028516ec1f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtextes_articles_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"textes_articles_df.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'textes_articles_df' is not defined"
]
}
],
"source": [
"textes_articles_df.to_csv(\"textes_articles_df.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"textes_articles_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}