Sentiment Analysis: Given a data of US Airline tweets and their sentiment. The task is to do sentiment analysis 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").
sandeepbansode/Support_Vector_Machine_U.S._Airline_tweets_Sentiment_Analysis
Sentiment Analysis: Given a data of US Airline tweets and their sentiment. The task is to do sentiment analysis 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").
Jupyter NotebookApache-2.0