Merge gitlab.viarezo.fr:2022difattagu/ei-twitter into testBranch

README.md

 # EI Twitter
 
+## Lancer une recherche 
 
+* Modifier la liste des termes et des termes à filtrer
+* Faire de même pour les filtres et les langues
+* Lancer le script
 
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.viarezo.fr/2022difattagu/ei-twitter.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.viarezo.fr/2022difattagu/ei-twitter/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
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+Le fichier de sortie est au format JSON dans le fichier `output`.

main.py

 import snscrape.modules.twitter as sntwitter
 import pandas as pd
 
-from src.functions import construct_query, convert_date_str, calc_frequency
+from src.functions import construct_query, convert_date_str, calc_frequency, supress_n
 
 table = pd.DataFrame(columns=['date', 'tweet'])
 
@@ -21,7 +21,7 @@ for i, tweet in enumerate(sntwitter.TwitterSearchScraper(query=construct_query(t
 
     if (tweet.lang in languages):
         dict = {'date': convert_date_str(
-            tweet.date), 'tweet': tweet.rawContent}
+            tweet.date), 'tweet': supress_n(tweet.rawContent)}
 
         if ("metaverse" in tweet.rawContent.lower()):
             table = pd.concat([table, pd.DataFrame.from_records([dict])])

src/functions.py

@@ -19,6 +19,11 @@ def construct_query(terms, negativeTerms, filters, negativeFilters):
     return (query)
 
 
+def supress_n(text):
+    str = text.replace('\n', ' ')
+    return (str)
+
+
 def convert_date_str(date):
     return (date.strftime(
             "%m/%d/%Y, %H:%M:%S"))

src/old_coding_weeks/emotions_fct.py

+
+"""
+    Ce programme permet d'analyser un dataframe de tweet (sentiments)
+    et de renvoyer des dataframes prêt à la visualisation
+"""
+import math
+import pandas as pd
+from nrclex import NRCLex
+from textblob import TextBlob
+
+
+def emo_analysis(dataframe):
+    """Analyse les sentiments présents dans les tweets fournis
+
+    Args:
+        dataframe (dataframe): dataframe avec les tweets
+
+    Returns:
+        list(dataframe,dataframe): listes de deux dataframes.
+        Le premier contient le traitement des emotions
+        et le second le traitement global
+    """
+
+    # on ne garde que les textes
+    data_text = dataframe['full_text']
+
+    # on crée une chaine de caractère contenant tous les textes concaténés
+    stack = data_text.str.cat(others=None, sep=None,
+                              na_rep=None, join='left')
+
+    # on calcule les occurences des mots par émotion
+    scores_dict = NRCLex(stack).raw_emotion_scores
+
+    # si aucun mot n'a été trouvés pour une catégorie, on crée la case et on la met à zéro
+    for word in ['fear', 'anger', 'joy', 'sadness', 'disgust', 'surprise', 'negative', 'positive']:
+        if word not in scores_dict:
+            scores_dict[word] = 0
+
+    # PARTIE 1 : EMOTIONS
+    # pour calculer la fréquence on a besoin d'un compteur des 6 émotions
+    emo_tot = scores_dict['fear'] + scores_dict['anger'] + scores_dict['joy'] + \
+        scores_dict['sadness'] + scores_dict['disgust'] + \
+        scores_dict['surprise']
+
+    # on crée le dataframe des émotions
+    dataframe1 = pd.DataFrame({
+        'Emotion': ['fear', 'anger', 'joy', 'sadness', 'disgust', 'surprise'],
+        'Frequency': [scores_dict['fear']/emo_tot,
+                      scores_dict['anger']/emo_tot,
+                      scores_dict['joy']/emo_tot,
+                      scores_dict['sadness']/emo_tot,
+                      scores_dict['disgust']/emo_tot,
+                      scores_dict['surprise']/emo_tot]
+    })
+
+    # PARTIE 2 : OTHER DATA
+    global_tot = scores_dict['positive'] + scores_dict['negative']
+    stack_tb = TextBlob(stack)
+
+    fav = dataframe['favorite_count']
+    fav_freq = fav.mean()/7500000
+    alpha = 2000000
+
+    # pour la fame, on fait une comparaison au tweet le plus liké : 7.5 millions
+    # on a donc un résultat entre 0 et 1
+    # on veut zoomer proche de zéro, et tasser proche de 1, en gardant des résultats entre 0 et 1
+    # on utilise la fonction f(x)=log(1+alpha*x)/log(1+alpha)
+
+    dataframe2 = pd.DataFrame({
+        'Global': ['Positivity', 'Subjectivity', 'Fame'],
+        'Frequency': [scores_dict['positive']/global_tot,
+                      stack_tb.sentiment.subjectivity,
+                      math.log(1+alpha*fav_freq)/math.log(1+alpha)]
+
+    })
+
+    return ([dataframe1, dataframe2])

src/old_coding_weeks/fetch_words.py

+
+"""
+A partir d'un dataframe, on fait une selection des 12
+mots les plus fréquents pour ensuite créer la mosaïque d'images.
+
+"""
+
+from collections import Counter
+from textblob import TextBlob
+
+
+def frequent_words(dataframe, num):
+    """recherche les n mots les plus fréquents dans
+    un dataframe de tweets
+
+    Args:
+        dataframe (datafram): contient les tweets
+        num (integer): nombre de mots recherchés
+
+    Returns:
+        list(strings): liste des mots n mots les plus fréquents
+    """
+    text = ""
+
+    for tweet in dataframe["full_text"]:
+
+        text += " " + tweet
+
+    text = text.lower()
+
+    # On enlève au texte les chaines de carctères inutiles qui reviennent souvent
+
+    remove = ["https://t.co/", "&amp", "@",
+              "▫️", "•", "'", " ’ ", "\"", " i ", " t ", " s "]
+    for mot in remove:
+        text = text.replace(mot, ' ')
+
+    text = TextBlob(text)
+
+    nouns_in_text = [w for (w, pos) in text.tags if (
+        pos[0] == 'N' or pos[0] == 'NN' or pos[0] == 'NNP' or pos[0] == 'NNS' or pos[0] == 'NNPS')]
+
+    # on enlève les mots de moins de 3 caractères
+    i = 0
+    while i < len(nouns_in_text):
+        if len(nouns_in_text[i]) < 3:
+            nouns_in_text.pop(i)
+        i = i+1
+
+    # On crée une liste avec des couples de la forme ("mot", nombre d'occurrences).
+    counter = Counter(nouns_in_text)
+    # on supprime le mot 's' qui apparait tout le temps
+    del counter['s']
+    # On prend dans la liste Counter les n mots les plus fréquents.
+    most_frequent = counter.most_common(num)
+
+    # Finalement, on fait une liste uniquement avec les mots.
+    most_frequent_words = [couple[0] for couple in most_frequent]
+
+    return most_frequent_words