Logistic Regression.

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)