Open AccessFBSC: An Analyzing Sentiments Using Fuzzy Based Bayesian Classification
Author(s) -
M. Karthica,
P. Sudarmani
Publication year - 2019
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2019.8.s1.1957
Subject(s) - thriving , computer science , sentiment analysis , bayesian probability , fuzzy logic , naive bayes classifier , data mining , service (business) , machine learning , social network service , artificial intelligence , data science , information retrieval , world wide web , social media , support vector machine , social science , economy , sociology , economics
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 user’s sentiment options topics in the twitter network. The sentiment classification is comparable to the user’s 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.