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A term correlation based semi-supervised microblog clustering with dual constraints

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Abstract

Microblog clustering is very important in many web applications. However, microblogs do not provide sufficient word occurrences. Meanwhile the limited length of these messages prevents traditional text clustering approaches from being employed to their full potential. To address this problem, in this paper, we propose a novel semi-supervised learning scheme fully exploring the semantic information to compensate for the limited message length. The key idea is to explore term correlation data, which well captures the semantic information for term weighting and provides greater context for microblogs. We then formulate microblog clustering problem as a semi-supervised non-negative matrix factorization co-clustering framework, which takes advantage of both prior domain knowledge of data points (microblogs) in the form of pair-wise constraints and category knowledge of features (terms). Our approach not only greatly reduces the labor-intensive labeling process, but also deeply exploits hidden information from microblog itself. Extensive experiments are conducted on two real-world microblog datasets. The results demonstrate the effectiveness of the proposed approach which produces promising performance as compared to state-of-the-art methods.

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Notes

  1. https://trends.google.com

  2. https://tartarus.org/martin/PorterStemmer/

  3. http://www.twittonary.com/

  4. http://twitterforteachers.wetpaint.com/page/Twitter+Dictionary

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61363058), Youth Science and Technology Support Program of Gansu Province (145RJYA259), Open Program of the Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (IIP2014-4), the Natural Science Foundation of Gansu Province for Distinguished Young Scholars (1308RJDA007) and the National Natural Science Foundation of China (No. 61762078).

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Authors and Affiliations

  1. College of Computer science and engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Huifang Ma, Di Zhang, Meihuizi Jia & Xianghong Lin

  2. The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100085, China

    Huifang Ma

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  1. Huifang Ma
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Correspondence to Huifang Ma.

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Ma, H., Zhang, D., Jia, M. et al. A term correlation based semi-supervised microblog clustering with dual constraints. Int. J. Mach. Learn. & Cyber. 10, 679–692 (2019). https://doi.org/10.1007/s13042-017-0750-0

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