Will get the global sentiment behind a word using Twitter.
-
Display percentage of happiness of a given word
-
Show sentiment count, word cloud and most used words.
-
Retrieve and display random tweets with its estimated sentiment.
To clone and run this application, you'll need Git and Flask installed on your computer. From your command line:
# Retrieve git folder
$ git clone https://github.com/Scylidose/TweetFeel.git
$ cd TweetFeel/
# Install dependencies
$ pip3 install -r requirements.txt
# Run application
$ make run
You can then access the application with the given address.
-
Comma-separated values (csv) file containing Sentiment140 dataset with 1.6 million tweets
https://www.kaggle.com/kazanova/sentiment140 -
Get atmost 100 tweets from a search query using Tweepy API in a JSON format.
-
Removed irrelevant punctuation, mentionned user, link and english stopwords.
-
Tokenized sentence.
-
Lemmatized tokens.
-
Deleted words longer than 15 characters.
-
TF-IDF Transformation.
-
Most frequent words and Bi-grams.
-
Count of estimated Positive and Negative tweets.
-
Displayed the WordCloud.
- Logistic Regression :
- L2 Penalty
- Tolerance value of 0.001
- C value of 1
-
Confusion Matrix
-
Accuracy classification score (Jaccard Score)