- Problem Definition
A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").
- Data
The data was downloaded from kaggle[https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment]
- Evaluation
The Evaluation metric is to get the best accuracy
- Features
The data Features include tweet_id, airline_sentiment, airline_sentiment_confidence, negativereason, negativereason_confidence, airline, airline_sentiment_gold, name, negativereason_gold, retweet_count, text, tweet_coord, tweet_created, tweet_location, user_timezone
- Modelling
The Model used are Support vector machine(SVM) and Random Forest Classifier (RFC)