Advertisement

SBTM: Topic Modeling over Short Texts

  • Jianhui Pang
  • Xiangsheng Li
  • Haoran Xie
  • Yanghui RaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)

Abstract

With the rapid development of social media services such as Twitter, Sina Weibo and so forth, short texts are becoming more and more prevalent. However, inferring topics from short texts is always full of challenges for many content analysis tasks because of the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a classification model named sentimental biterm topic model (SBTM), which is applied to sentiment classification over short texts. To alleviate the problem of sparsity in short texts, the similarity between words and documents are firstly estimated by singular value decomposition. Then, the most similar words are added to each short document in the corpus. Extensive evaluations on sentiment detection of short text validate the effectiveness of the proposed method.

Keywords

Short text classification Sentiment detection Topic-based similarity Biterm topic model 

Notes

Acknowledgements

The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper. This research has been supported by the National Natural Science Foundation of China (61502545, 61472453, U1401256, U1501252), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14), and the Fundamental Research Funds for the Central Universities.

References

  1. 1.
    Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using wikipedia. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 787–788. ACM (2007)Google Scholar
  2. 2.
    Bao, S., Xu, S., Zhang, L., Yan, R., Su, Z., Han, D., Yu, Y.: Mining social emotions from affective text. IEEE Trans. Knowl. Data Eng. 24(9), 1658–1670 (2012)CrossRefGoogle Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Cheng, X., Lan, Y., Guo, J., Yan, X.: Btm: Topic modeling over short texts. IEEE Trans. Knowl. Data Eng. 26(12), 2928–2941 (2014)CrossRefGoogle Scholar
  5. 5.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)CrossRefGoogle Scholar
  6. 6.
    Gangemi, A., Presutti, V., Reforgiato Recupero, D.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comput. Intell. Mag. 9(1), 20–30 (2014)CrossRefGoogle Scholar
  7. 7.
    Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 50–57 (1999)Google Scholar
  9. 9.
    Jin, O., Liu, N.N., Zhao, K., Yu, Y., Yang, Q.: Transferring topical knowledge from auxiliary long texts for short text clustering. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM), pp. 775–784. ACM (2011)Google Scholar
  10. 10.
    Katz, P., Singleton, M., Wicentowski, R.: Swat-mp: The semeval-2007 systems for task 5 and task 14. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval), pp. 308–313. Association for Computational Linguistics (2007)Google Scholar
  11. 11.
    Kim, S.B., Han, K.S., Rim, H.C., Myaeng, S.H.: Some effective techniques for naive bayes text classification. IEEE Trans. Knowl. Data Eng. 18(11), 1457–1466 (2006)CrossRefGoogle Scholar
  12. 12.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)Google Scholar
  13. 13.
    Phan, X.H., Nguyen, L.M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th International Conference on World Wide Web (WWW), pp. 91–100. ACM (2008)Google Scholar
  14. 14.
    Rao, Y., Lei, J., Liu, W., Li, Q., Chen, M.: Building emotional dictionary for sentiment analysis of online news. World Wide Web J. 17(4), 723–742 (2014)CrossRefGoogle Scholar
  15. 15.
    Rao, Y., Li, Q., Liu, W., Wu, Q., Quan, X.: Affective topic model for social emotion detection. Neural Netw. 58(5), 29–37 (2014)CrossRefGoogle Scholar
  16. 16.
    Rao, Y., Li, Q., Mao, X., Liu, W.: Sentiment topic models for social emotion mining. Inf. Sci. 266(5), 90–100 (2014)CrossRefGoogle Scholar
  17. 17.
    Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th International Conference on World Wide Web (WWW), pp. 377–386(2006)Google Scholar
  18. 18.
    Snow, R., O’Connor, B., Jurafsky, D., Ng, A.Y.: Cheap and fast–but is it good?: evaluating non-expert annotations for natural language tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 254–263. Association for Computational Linguistics (2008)Google Scholar
  19. 19.
    Stoyanov, V., Cardie, C.: Annotating topics of opinions. In: Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC), pp. 3213–3217 (2008)Google Scholar
  20. 20.
    Strapparava, C., Mihalcea, R.: Semeval-2007 task 14: affective text. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval), pp. 70–74. Association for Computational Linguistics (2007)Google Scholar
  21. 21.
    Turney, P.D.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL). pp. 417–424 (2002)Google Scholar
  22. 22.
    Wang, J., Yao, Y., Liu, Z.: A new text classification method based on hmm-svm. In: International Symposium on Communications and Information Technologies (ISCIT). pp. 1516–1519 (2007)Google Scholar
  23. 23.
    Xie, H., Li, Q., Cai, Y.: Community-aware resource profiling for personalized search in folksonomy. J. Comput. Sci. Technol. 27(3), 599–610 (2012)CrossRefzbMATHGoogle Scholar
  24. 24.
    Xie, H., Li, Q., Mao, X., Li, X., Cai, Y., Rao, Y.: Community-aware user profile enrichment in folksonomy. Neural Netw. 58, 111–121 (2014)CrossRefGoogle Scholar
  25. 25.
    Xie, H., Li, Q., Mao, X., Li, X., Cai, Y., Zheng, Q.: Mining latent user community for tag-based and content-based search in social media. Comput. J. 57(9), 1415–1430 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jianhui Pang
    • 1
  • Xiangsheng Li
    • 1
  • Haoran Xie
    • 2
  • Yanghui Rao
    • 1
    Email author
  1. 1.Sun Yat-sen UniversityGuangzhouChina
  2. 2.Caritas Institute of Higher EducationNew TerritoriesHong Kong

Personalised recommendations