A COMPREHENSIVE ANALYSIS ON SENTIMENTAL DATA SET USING MACHINE LEARNING TECHNIQUE

The Internet changed the way through which people communicate and also express their opinions and their emotions. Be that as it may, the information via webbased networking media isn't extremely justifiable and cannot be easily understood for effective data mining, this is because these days individuals on the web utilize diverse dialects and web slang to express their emotions and feelings. In this survey, we are making use of “Naïve Bayes” [7] the algorithm to classify our Twitter data. Using this algorithm we are going to do opinion mining on “Goods and Service taxes (GST)” which is currently most discussed topic on social media websites. In this paper, we associate our twitter record to R tool to extricate information from the twitter and with the utilization of "Naive Bayes" calculation we perform sentimental analysis on about 10,000 tweets and show our outcomes graphically for client understandability.