Email this article 
Open Access
Subscription or Fee Access
An Efficient Naive Bayes Classification for Sentiment Analysis on Twitter
Abstract
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.
Keywords
References
Shulong Tan, Yang Li, Huan Sun, Ziyu Guan, Xifeng Yan,, Jiajun Bu, Chun Chen,and Xiaofei He, ”Interpret the public sentiment variations on twitter”, ieee transactions on knowledge and data engineering, vol. 6, no. 1, september 2012
J. Bollen, H. Mao, and A. Pepe. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proc. of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, 2011.
J. Weng and B.-S. Lee, ―Event detection in twitter,‖ in Proc. 5thInt. AAAI Conf. Weblogs Social Media, Barcelona, Spain, 2011
B. Pang and L. Lee, ―Opinion mining and sentiment analysis,‖Found. Trends Inform. Retrieval, vol. 2, no.(1–2), pp. 1–135, 2008.
T. Sakaki, M. Okazaki, and Y. Matsuo, ―Earthquake shakes twitter users: Real-time event detection by social sensors,‖ in Proc. 19thInt. Conf. WWW, Raleigh, NC, USA, 2010.
Y. Hu, A. John, F. Wang, and D. D. Seligmann, ―Et-lda: Joint topic modeling for aligning events andtheir twitter feedback,‖ in Proc.26th AAAI Conf. Artif. Intell., Vancouver, BC, Canada, 2012.
L. Zhuang, F. Jing, X. Zhu, and L. Zhang, ―Movie review mining and summarization,‖ in Proc. 15th ACM Int. Conf. Inform.Knowl.Manage., Arlington, TX, USA, 2006.
Y. Hu, A. John, F. Wang, and D. D. Seligmann. Et-lda: Joint topic modeling for aligning events and their twitter feedback. In Proc. of the 26th AAAI Conference on Artificial ntelligence, Vancouver, Canada, 2012.
M. Thelwall, K. Buckley, and G. Paltoglou.Sentiment in twitter events.Journal of the American Society for Information Science andTechnology, 62(2):406–418, 2011.
Pedro Domingos and Michael Pazzani. Beyond independence: conditions for the optimality of the simple Bayesian classifier. In Proceedings of the Thirteenth International Conference on Machine Learning (ICML), 1996.
M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. Sentiment strength detection in short informal text.Journal of the American Society for Information Science and Technology, 61(12):2544–2558, 2010.
X. Wang, F. Wei, X. Liu, M. Zhou, and M. Zhang. Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In Proc. of the 20th ACM CIKM, Glasgow, Scotland, 2011.
A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. Technical report, 2009.
L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao. Target dependent twitter sentiment classification. In Proc. of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, 2011.
Shulong Tan, Yang Li, Huan Sun, Ziyu Guan, Xifeng Yan, ―Interpreting the Public Sentiment Variations on Twitter,‖ IEEE Transactions on Knowledge and Data Engineering, VOL. 26, NO.5, MAY 2014.
Pedro Domingos and Michael Pazzani. Beyond independence: conditions for the optimality of the simple Bayesian classifier. In Proceedings of the Thirteenth International Conference on Machine Learning (ICML), 1996.
M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. Sentiment strength detection in short informal text.Journal of the American Society for Information Science and Technology, 61(12):2544–2558, 2010.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.