Abstract
The thriving Micro blog service, Twitter, attracts more people to post their feelings and opinions on various topics. Millions of users share opinions on totally different aspects of life on a daily basis. It observing the userβs sentiment options topics in the twitter network. The sentiment classification is comparable to the userβs opinions that are based on dynamic manner. An optimal Fuzzy based Bayesian classification is a capable way that has been proposed to improve the classification accuracy, unless the large amount of information on these platforms make them viable for use as data sources, in applications based on sentiment analysis. The research work developed a Fuzzy based Bayesian sentiment classification (FBSC) based dynamic online twitter search data architecture that ensures truthful positive, negative and neutral results.
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