FBSC: An Analyzing Sentiments Using Fuzzy Based Bayesian ClassificationAuthor : M. Karthica and P. Sudarmani
Volume 8 No.1 Special Issue:February 2019 pp 58-62
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.
Data Mining, Twitter, Sentiment Analysis, Bayesian Classification.
 Hu .M and B. Liu, “Mining and Summarizing Customer Reviews,” Proc. 10th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’04)., pp. 168-177, 2004.
 Pantel .P and D. Ravichandran, “Automatically Labeling Semantic Classes,” Proc. Conf. North Am. Ch. Assoc. for Computational Linguistics: Human Language Technologies (NAACL-HLT ’04)., pp. 321-328, 2004.
 Shen .D, J. Wu, B. Cao, J.-T. Sun, Q. Yang, Z. Chen, and Y. Li, “Exploiting Term Relationship to Boost Text Classification,” Proc. 18th ACM Conf. Information and Knowledge Management (CIKM ’09)., pp. 1637-1640, 2009.
 Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, “Predicting elections with twitter: What 140 characters reveal about political sentiment,” in Proc. 4th Int. AAAI Conf. Weblogs Soc. Media., Vol. 10, pp. 178–185, 2010.
 L. T. Nguyen, P. Wu, W. Chan, W. Peng, and Y. Zhang, “Predicting collective sentiment dynamics from time-series social media,” in Proc. 1st Int. Workshop Issues Sentiment Discovery Opinion Mining., pp.6, 2012,
 M. Thelwall, K. Buckley, and G. Paltoglou, “Sentiment in twitter events,” J. Am. Soc. Inform. Sci. Technol., Vol. 62, No. 2, pp. 406–418, 2011.
 A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, “Sentiment analysis of twitter data,” in Proc. Workshop Lang. Soc. Media., 2011, pp. 30–38.
 S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain sentiment classification via spectral feature alignment,” in Proc. 19th Int. Conf. World Wide Web., pp. 751–760, 2010.
 A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Computer Science Department, Stanford, USA., pp. 1–12, 2009.
 X. Wan, “Co-training for cross-lingual sentiment classification,” in Proc. Joint Conf. 47th Annu. Meeting ACL 4th Int.Joint Conf. Natural Language Process. AFNLP., Vol. 1, pp. 235–243, 2009.