An Efficient Naive Bayes Classification for Sentiment Analysis on Twitter
P. Shanmuganathan, C.R. Sakthivel · CiiT international journal of data mining and knowledge engineering · 2015
Twitter is one of the large amounts of tweets contained social media site. That’s being as a good platform for tracking and analyzing public sentiment. Nowadays the one of the important data mining research is sentiment analysis mining or opinion mining analyzing. That’s why the sentiment analysis is attracting the researchers to find the critical information for decision making purpose in both of academic sides and also industry sides. This proposed system finds the sentiment variations. It implements existing FB-LDA and RCB-LDA algorithm with new Naive Bayes algorithm using C# .Net for effectively handle the class imbalance Problem of positive and negative changes made by improper sentiment label assignment also improves the accuracy significantly than the existing system.