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JIAYUE LIU 4 years ago
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  1. 212
      analyse_twitter_LIU.ipynb
  2. 1
      test.txt
  3. 1194
      tweets_database.csv
  4. BIN
      twitter_network_mapping_degree.pdf
  5. BIN
      twitter_network_mapping_follower.pdf

212
analyse_twitter_LIU.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Identifier les leaders d’opinion du domaine de l’IA sur Twitter\n",
"\n",
"Auteur : Jiayue LIU (MSc Data Management, Paris School of Business)\n",
"\n",
"Date : 18 Avril 2021 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Installer toutes les librairies nécessaires à l'exercice\n",
"import tweepy\n",
"import pandas as pd\n",
"pd.options.mode.chained_assignment = None\n",
"import igraph as ig\n",
"import datetime\n",
"\n",
"# Authentification API\n",
"auth = tweepy.OAuthHandler(\n",
" '***', \n",
" '***')\n",
"auth.set_access_token(\n",
" '***', \n",
" '***')\n",
"api = tweepy.API(auth,wait_on_rate_limit=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extraire les tweets contenant les mots-clés définis\n",
"hashtags = ['#IA', '#IntelligenceArtificielle']\n",
"results = tweepy.Cursor(api.search, q=hashtags, lang='fr').items()\n",
"\n",
"# Convertir les résultats de recherche du json en dataframe\n",
"json_data = [r._json for r in results]\n",
"results_df = pd.json_normalize(json_data)\n",
"\n",
"results_df.to_csv(\"tweets_database.csv\", sep=\",\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Garder des informations qui nous intéresseraient en renommant les colonnes\n",
"simple_results = results_df[['created_at',\n",
" 'user.location',\n",
" 'user.screen_name',\n",
" 'user.followers_count',\n",
" 'entities.user_mentions']]\n",
"simple_results.columns = ['time',\n",
" 'location',\n",
" 'user_id',\n",
" 'num_followers',\n",
" 'mentions']\n",
"\n",
"# Afficher le résultat brute mais simplifié\n",
"today = datetime.date.today()\n",
"week_ago = today - datetime.timedelta(days=7)\n",
"print(\"Pendant la semaine du\", week_ago.strftime(\"%d/%m/%Y\"),\n",
" \"au\", today.strftime(\"%d/%m/%Y\"),\n",
" \", les tweets en français et ayant pour hashtags #IA ou #IntelligenceArtificielle sont les suivants : \\n\",\n",
" simple_results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Convertir la colonne \"mentions\" en liste simple\n",
"mentioned_users = []\n",
"for mention in simple_results.mentions:\n",
" mentioned_users.append(list(map(lambda d: d['screen_name'], mention)))\n",
"simple_results['mentions'] = mentioned_users\n",
"\n",
"# Stocker tous les edges et nodes dans des dataframes\n",
"edges_df = simple_results.loc[:, ['mentions', 'user_id', 'num_followers']]\n",
"edges_df = edges_df.explode('mentions').reset_index().drop('index',1)\n",
"\n",
"mention_list = edges_df.mentions.to_list()\n",
"user_list = edges_df.user_id.to_list()\n",
"nodes_list = set(user_list + mention_list)\n",
"\n",
"edges = edges_df.dropna().reset_index().drop('index',1)\n",
"nodes = pd.DataFrame(nodes_list)\n",
"nodes.columns = (['user_id'])\n",
"nodes = pd.merge(nodes, edges, on='user_id', how='left')\n",
"nodes = nodes.drop(columns=['mentions']).groupby(by='user_id').mean().reset_index()\n",
"\n",
"print(\"La liste des mentions entre les utilisateurs : \\n\",\n",
" edges)\n",
"print(\"La liste des utilisateurs Twitter ayant publié du contenu relatif à l'IA durant la semaine passée : \\n\",\n",
" nodes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Générer le graphe représentant le réseau social avec le package iGraph\n",
"\n",
"kol_map = ig.Graph.DataFrame(edges,\n",
" directed = True,\n",
" vertices = nodes)\n",
"kol_map.vs['name'] = nodes['user_id']\n",
"kol_map.vs['num_followers'] = nodes['num_followers']*0.001"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Comparer le nombre d'abonnés des utilisateurs du réseau\n",
"nodes['num_followers'] = nodes['num_followers'].astype(pd.Int64Dtype())\n",
"rank_followers = nodes.sort_values(by='num_followers',\n",
" ascending=False)\n",
"rank_followers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculer la centralité de degré en utilisant le package igraph\n",
"out_degrees = pd.DataFrame({'node': nodes['user_id'],\n",
" 'degree':kol_map.degree(mode=\"out\")})\n",
"out_degrees = out_degrees.sort_values(by='degree',\n",
" ascending=False)\n",
"\n",
"print(\"Les dix comptes Twitter ayant été le plus mentionnés durant la semaine passée sont : \\n\",\n",
" out_degrees.head(10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Apppliquer la méthode \"Fruchterman-Reingold force-directed\" pour construire le réseau\n",
"layout = kol_map.layout('fr')\n",
"\n",
"visual_style = {}\n",
"visual_style[\"vertex_size\"] = kol_map.degree()\n",
"visual_style[\"vertex_color\"] = \"#1DA1F2\"\n",
"visual_style[\"vertex_label\"] = kol_map.vs[\"name\"]\n",
"visual_style[\"vertex_label_size\"] = 5\n",
"visual_style[\"edge_arrow_size\"] = 0.5\n",
"visual_style[\"layout\"] = layout\n",
"visual_style[\"bbox\"] = (500, 500)\n",
"visual_style[\"margin\"] = 20\n",
"\n",
"kol_map0 = kol_map.copy()\n",
"visual_style0 = visual_style.copy()\n",
"visual_style0[\"vertex_size\"] = kol_map.vs['num_followers']\n",
"\n",
"# Afficher et sauvegarder les graphes générés\n",
"print(\"Carte représentant le réseau d'influence des comptes Twitter du domaine de l'IA : \\n\",\n",
" \"(la taille des noeuds est proportionnelle à leur degré sortant) \\n\")\n",
"ig.plot(kol_map, \"twitter_network_mapping_degree.pdf\", **visual_style)\n",
"\n",
"print(\"Carte représentant le réseau d'influence des comptes Twitter du domaine de l'IA : \\n\"\n",
" \"(la taille des noeuds est proportionnelle à leur nombre d'abonnés) \\n\")\n",
"ig.plot(kol_map0, \"twitter_network_mapping_follower.pdf\", **visual_style0)"
]
}
],
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

1
test.txt
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Hello Gitea

1194
tweets_database.csv
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BIN
twitter_network_mapping_degree.pdf
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twitter_network_mapping_follower.pdf
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