An Extensive study of Sentiment Analysis tools and Binary Classification of tweets using Rapid Miner

The online content includes conversation in social networking websites, tweets, blogs and various forums discussing occasions, people and everything which exist in this world. With the huge growth of the online content, high rated information is achievable. Customer feedbacks help organizations to improve their services by rectifying their drawbacks. Manual analysis of information in the era of big data would be a cumbersome task. Countless tools are available for mining information/sentiment from World Wide Web but the choice of tool is the biggest problem at present time. One should have an idea about the framework to evaluate tools. With the evaluation structure for the tools, this paper will compare twenty tools for text analysis with respect to their applications and extension availability for Sentiment Analysis (SA). An experiment is performed in Rapid Miner to derive Sentiment from tweets and accuracies of different algorithms are compared to find out the best performing one.