Abstract
Social media such as Twitter, has become a valuable source for mining opinions of users about all kinds of topics. In this paper, we investigate how to automatically integrate topic related opinions expressed by a user in User-Generated Content (UGC). We propose a general subjectivity model by combining topics and fine-grained opinions towards each topic, and design an efficient algorithm to establish the model. We demonstrate utility of our model in the opinion prediction problem and verify the effectiveness of our model qualitatively and quantitatively in a series of experiments on real Twitter data. Results show that the proposed model is effective and can generate consistent integrated opinion summaries for users. Furthermore, the proposed model is more suitable for social media context, thus can reach better performance in an opinion prediction task.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Lu, Y., Zhai, C.: Opinion integration through semi-supervised topic modeling. In: Proceedings of the 17th International Conference on World Wide Web, pp. 121–130. ACM (2008)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 36–44. Association for Computational Linguistics (2010)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 241–249. Association for Computational Linguistics (2010)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151–160. Association for Computational Linguistics (2011)
Li, G., Hoi, S.C., Chang, K., Jain, R.: Micro-blogging sentiment detection by collaborative online learning. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 893–898. IEEE (2010)
Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1397–1405. ACM (2011)
Mostafa, M.M.: More than words: Social networks text mining for consumer brand sentiments. Expert Systems with Applications 40(10), 4241–4251 (2013)
Malouf, R., Mullen, T.: Taking sides: User classification for informal online political discourse. Internet Research 18(2), 177–190 (2008)
Liu, H., Zhao, Y., Qin, B., Liu, T.: Comment target extraction and sentiment classification. Journal of Chinese Information Processing 24(1), 84–89 (2010)
Zhai, Z., Liu, B., Xu, H., Jia, P.: Constrained LDA for grouping product features in opinion mining. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part I. LNCS (LNAI), vol. 6634, pp. 448–459. Springer, Heidelberg (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 487–494. AUAI Press (2004)
Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 248–256. Association for Computational Linguistics (2009)
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)
Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 375–384. ACM (2009)
Xie, S., Tang, J., Wang, T.: Resonance elicits diffusion: Modeling subjectivity for retweeting behavior analysis. Cognitive Computation, 1–13 (2014)
Walton, D.N.: Bias, critical doubt and fallacies. Argumentation and Advocacy 28, 1–22 (1991)
Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: KDD, pp. 1023–1031 (2012)
Lazarsfeld, P.F., Merton, R.K.: Friendship as a social process: A substantive and methodological analysis. In: Berger, M., Abel, T. (eds.) Freedom and Control in Modern Society. Van Nostrand, New York (1954)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology, 415–444 (2001)
Thelwall, M.: Emotion homophily in social network site messages. First Monday 15(4) (2010)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. Association for Computational Linguistics (2005)
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61(12), 2544–2558 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xie, S., Tang, J., Wang, T. (2014). Topic Related Opinion Integration for Users of Social Media. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-662-45558-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45557-9
Online ISBN: 978-3-662-45558-6
eBook Packages: Computer ScienceComputer Science (R0)