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2016 | OriginalPaper | Buchkapitel

SBTM: Topic Modeling over Short Texts

verfasst von : Jianhui Pang, Xiangsheng Li, Haoran Xie, Yanghui Rao

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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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.

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Literatur
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)
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)CrossRef
3.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRefMATH
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)
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)
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)
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)CrossRef
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)
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)
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)CrossRef
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)CrossRef
16.
Rao, Y., Li, Q., Mao, X., Liu, W.: Sentiment topic models for social emotion mining. Inf. Sci. 266(5), 90–100 (2014)CrossRef
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)
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)
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)
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)
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)
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)
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)CrossRefMATH
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)CrossRef
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)CrossRef
Metadaten
Titel
SBTM: Topic Modeling over Short Texts
verfasst von
Jianhui Pang
Xiangsheng Li
Haoran Xie
Yanghui Rao
Copyright-Jahr
2016
Buch
Database Systems for Advanced Applications

Print ISBN: 978-3-319-32054-0

Electronic ISBN: 978-3-319-32055-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-32055-7_4