diff --git a/README.md b/README.md
index c32d326ea9da6d5860eee1df9ba2c46fa26cd8fd..32bea23660a42ea883bdcf75770752de9a7602d5 100644
--- a/README.md
+++ b/README.md
@@ -1,92 +1,9 @@
# 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.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+Le fichier de sortie est au format JSON dans le fichier `output`.
diff --git a/main.py b/main.py
index c6950e0c8008236b7ca12d033bb1814df15371c2..dbf131e15463c7dd1cd8de4f3d81896eafcc5a4c 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,7 @@
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])])
diff --git a/src/functions.py b/src/functions.py
index 6ec09c7c7e9edc78e42e6daa3e6364cd3d5fdba5..0497f390b13773fe41d536bae04753a26fb22d41 100644
--- a/src/functions.py
+++ b/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"))
diff --git a/src/old_coding_weeks/emotions_fct.py b/src/old_coding_weeks/emotions_fct.py
new file mode 100644
index 0000000000000000000000000000000000000000..e502aa86cc64c7c7dbbb8fccb2b571c3d489660a
--- /dev/null
+++ b/src/old_coding_weeks/emotions_fct.py
@@ -0,0 +1,77 @@
+
+"""
+ 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])
diff --git a/src/old_coding_weeks/fetch_words.py b/src/old_coding_weeks/fetch_words.py
new file mode 100644
index 0000000000000000000000000000000000000000..85b0160f73b2fb6962966b0b9ab7477c2cc5cc88
--- /dev/null
+++ b/src/old_coding_weeks/fetch_words.py
@@ -0,0 +1,60 @@
+
+"""
+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/", "&", "@",
+ "▫️", "•", "'", " ’ ", "\"", " 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