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International Conference on Web Information Systems Engineering

WISE 2016: Web Information Systems Engineering – WISE 2016 pp 109–123Cite as

Domain Dictionary-Based Topic Modeling for Social Text

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10041))

Abstract

Online social networks are becoming increasingly popular and posting large volumes of unstructured social text documents every day. Inferring topics from large-scale social texts is a significant but challenging task for many text mining applications. Conventional topic models has been shown unsatisfactory results due to the sparsity and noise of content in short texts. Besides, the learned topics are very difficult to understand the semantic information only by the top weighted terms. In this paper, we propose a novel social text topic modeling method to deal with the problems. The proposed model utilizes topic domain dictionary to construct a weakly supervised matrix, which can play a role of making reference matrix and the learned topic matrix become similar. Experimental results on the constructed social text dataset from Twitter demonstrate that our proposed method can outperform the state-of-the art baselines significantly and also improve the semantic relevancy of the learned topic.

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Notes

  1. 1.

    http://www.ldoceonline.com/.

  2. 2.

    http://www.csie.ntu.edu.tw/cjlin/liblinear/.

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Acknowledgments

This work was supported by National Key Technology R&D Program(No. 2012BAH46B03), and the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences(No. XDA06030200).

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Authors and Affiliations

  1. National Engineering Laboratory for Information Security Technologies, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Bo Jiang, Jiguang Liang, Ying Sha, Rui Li & Lihong Wang

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  1. Bo Jiang
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  2. Jiguang Liang
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  3. Ying Sha
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  4. Rui Li
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  5. Lihong Wang
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Corresponding author

Correspondence to Ying Sha .

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznan, Poland

    Wojciech Cellary

  2. University of Minnesota, Minneapolis, Minnesota, USA

    Mohamed F. Mokbel

  3. Tsinghua University, Beijing, China

    Jianmin Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Victoria University, Melbourne, Victoria, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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Jiang, B., Liang, J., Sha, Y., Li, R., Wang, L. (2016). Domain Dictionary-Based Topic Modeling for Social Text. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-48740-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48739-7

  • Online ISBN: 978-3-319-48740-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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