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SBTM: Topic Modeling over Short Texts

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9645))

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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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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.

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Authors and Affiliations

  1. Sun Yat-sen University, Guangzhou, China

    Jianhui Pang, Xiangsheng Li & Yanghui Rao

  2. Caritas Institute of Higher Education, New Territories, Hong Kong

    Haoran Xie

Authors
  1. Jianhui Pang
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  2. Xiangsheng Li
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  3. Haoran Xie
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  4. Yanghui Rao
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Corresponding author

Correspondence to Yanghui Rao .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Hong Gao

  2. Kangwon National University, Kangwon, Korea (Republic of)

    Jinho Kim

  3. Kumamoto University, Kumamoto-shi, Japan

    Yasushi Sakurai

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© 2016 Springer International Publishing Switzerland

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Pang, J., Li, X., Xie, H., Rao, Y. (2016). SBTM: Topic Modeling over Short Texts. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_4

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32054-0

  • Online ISBN: 978-3-319-32055-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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