A Machine Learning Approach to Extract Opinions from Social Media Content

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The Opinion Mining (OM) from mobile based social media content (SMC) is more challenging compared to topic-based mining, and it cannot be performed based on just examining the presence of single words in the text containing opinion expressions. Moreover, the existing systems of opinion   classification find that a large number of features that are not feasible for the mobile environment. The existing methods of OM in this mobile environment do not consider the semantic orientation of the SMC in the review. The proposed machine learning approach extends the feature-based classification approach to identify the orientation of the phrase on taking context into account to improve the accuracy. 



  • Keywords

    LDA, Latent Dirichlet Allocation, Opinion Mining (OM), CISDL, Mobile platform, social media content

  • References

      [1] Bing Liu, “Sentiment Analysis and Opinion Mining” Morgan & Claypool Publishers, pp. 1- 168, 2012

      [2] Bo Pang and Lillian Lee, “Opinion mining and sentiment analysis” Foundations and Trends in Information Retrieval, Vol. 2, No 1- 2, 1–135, 2008

      [3] P. D. Turney, “Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews,”in Proceedings of 40th ACM Annual Meeting on Association for Computational Linguistics, pp. 417–424, 2002.

      [4] A. Esuli and F. Sebastiani, “Determining the semantic orientation of terms through gloss classification,” in Proceedings of 14th ACM International Conference on Information Knowledge Management, pp. 617- 624, 2005.

      [5] B. Pang, L. Lee, and S.Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proceedings of ACM Conference on Empirical Methods in Natural Language Processing, Vol. 10, pp. 79–86, 2002.

      [6] Chien-Liang Liu, Wen-Hoar Hsaio, Chia-Hoang Lee, Gen-Chi Lu, and Emery Jou, “Movie Rating and Review Summarization in Mobile Environment”, IEEE transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 42, No. 3, pp. 397- 407, 2012

      [7] Ellen Riloff, Janyce Wiebe, and William Phillips. “Exploiting subjectivity classification to improve information extraction” In Proceedings of 20th ACM national conference on Artificial Intelligence (AAAI), pp. 1106–1111, 2005.

      [8] Yohei Seki, Koji Eguchi, Noriko Kando, and Masaki Aono. “Multi-document summarization with subjectivity analysis at DUC 2005”. In Proceedings of the Document Understanding Conference (DUC), 2005.

      [9] V. N. Vapnik, “The Nature of Statistical Learning Theory”. Springer-Verlag, Information science and statistics, pp. 1- 314, 2000.

      [10] Danushka Bollegala, David Weir, and John Carroll, “Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus”, IEEE transactions on Knowledge and Data Engineering, Vol. 25, No. 8, pp. 1719- 1731, 2013

      [11] A. Esuli and F. Sebastiani, “SENTIWORDNET: A publicly available lexical resource for opinion mining,” in proceedings of 5th Conference on Language Resources and Evalaution, pp. 417–422, 2006.

      [12] Alexander Pak, and Patrick Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining” pp. 1320- 1326, 2010.

      [13] Shulong Tan, Yang Li, Huan Sun, Ziyu Guan, Xifeng Yan, Jiajun Bu, Chun Chen, an Xiaofei He, “Interpreting the Public Sentiment Variations on Twitter” IEEE transactions on Knowledge and Data Engineering, Vol. 26, No. 5, pp. 1158- 1170, 2014

      [14] Chenghua Lin, Yulan He, Richard Everson, Member, IEEE, and Stefan Ruger, “Weakly Supervised Joint Sentiment-Topic Detection from Text” IEEE transactions on Knowledge and Data Engineering, Vol. 24, No. 6, pp. 1134- 1145, 2012

      [15] Amazon customer - reviews , http://www.amazon.in/gp/cdp/member-rviews/A3A9YE4SZADQ5W/ref=cm_cr_tr_tbl_1_sar?ie=UTF8&sort_by=MostRecentReview

      [16] Jiang, Long, et al. "Target-dependent twitter sentiment classification."Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, 2011.

      [17] EBay product review in mobile app,http://pages.ebay.com/sellerinformation/news/sprupd16/product-reviews.html

      [18] Salina Adinarayana, E.Ilavarasan, ”An Efficient Decision Tree for Imbalance data learning using Confiscate and Substitute Technique.”, Materials Today: Proceedings , pp. 680-687,2018 Volume 5,Issue 1P1,ISSN 2214-7853




Article ID: 20080
DOI: 10.14419/ijet.v7i4.5.20080

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.