Abstract
Opinions or information can be shared on social media sites including, LinkedIn, blogs, Facebook, twitter, etc., in text form. Opinions or views about movies, products, politics or any interested topics of user can be shared using social networking sites in comments or feedback or picture form. Individuals opinion about political events, social, issues and products can be gathered and analysed by sentiment analysis. The proposed system includes preprocessing, feature extraction, sentiment classification using hybrid Bayes theorem support vector machine (HBSVM) algorithm. Preprocessing is used for removing unnecessary data, and it helps to improve the classification accuracy in the given dataset. Then, feature extraction is performed to select the prominent features based on the frequent terms. Then, apply HBSVM model for classifying neutral and non-neutral posts. Negative and positive are the class of non-neutral posts. Based on response to a post of various aspects, classification of group members is done. High performance is exhibited by proposed HBSVM as proven by results of experimentation when compared with existing techniques.
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References
Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)
Melville, P., Gryc, W. and Lawrence, R.D.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2009)
Lee, S.H., Cui, J., Kim, J.W.: Sentiment analysis on movie review through building modified sentiment dictionary by movie genre. J. Intell. Inform. Syst. 22(2), 97–113 (2016)
Suresh, H.: An unsupervised fuzzy clustering method for twitter sentiment analysis. In: 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE (2016)
Narayanan, R., Liu, B., Choudhary, A.: Sentiment analysis of conditional sentences. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2009), Singapore (2009)
Karampiperis, P., Koukourikos, A., Stoitsi, G.: Collaborative filtering recommendation of educational content in social environments utilizing sentiment analysis techniques. In: Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, vol. RecSysTEL Edited Volume. Springer, (2013)
Rathi, M., et al.: Sentiment analysis of tweets using machine learning approach. In: 2018 Eleventh International Conference on Contemporary Computing (IC3). IEEE, (2018)
Gautam, G., Yadav, D.: Sentiment Analysis of Twitter Data Using Machine Learning Approaches and Semantic Analysis. Department of Computer Science & Engineering, IEEE (2014)
Roychowdhury, S.: Expediting K-means Cluster Analysis Data Mining using Subsample Elimination Preprocessing. U.S. Patent No. 8,229,876. 24 Jul. 2012
Mukherjee, S., Bhattacharyya P.: Feature specific sentiment analysis for product reviews. In: International Conference on Intelligent Text Processing and Computational Linguistics. Springer, Berlin, Heidelberg (2012)
Zheng, W., Ye, Q.: Sentiment classification of Chinese traveler reviews by support vector machine algorithm. In: 2009 Third International Symposium on Intelligent Information Technology Application, vol. 3. IEEE (2009)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
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Shekhar, S., Mohan, N. (2021). Sentiment Classification Using Hybrid Bayes Theorem Support Vector Machine Over Social Network. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K., Suryani, E. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1168. Springer, Singapore. https://doi.org/10.1007/978-981-15-5345-5_10
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DOI: https://doi.org/10.1007/978-981-15-5345-5_10
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