Twitter Sentiment Analysis

0: Positive Tweet
1: Negative Tweet

The project flow is as follow:

1 Data Cleaning
  - Data Preprocessing: In here tweets are cleaned and few concepts of     Natural Language Processing are used like Stemming.
2 Data Analysis and Visualization
  - WordClouds are generated of all the tweets, positive tweets and     negative tweets
  - Tweets are also analysed by the hashtags used, and a bargraph is     made for comparing their frequency
3 Data Modeling
   - Logistic Regression
   - Naive Bayes
   To improve the score, Bag of Words and TFIDF Vectorizer is used and    models are applied
   - Logistic Regression
   - Naive Bayes
   - Support  Vector Machine
   - Random Forest Classifier