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A Hybrid Approach for Sparse Data Classification Based on Topic Model

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Book cover Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9998))

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Abstract

With an increasing number of short text emerging, sparse text classification is becoming crucial in data mining and information retrieval area. Many efforts have been devoted to improve the efficiency of normal text classification. However, it is still immature in terms of high-dimension and sparse data processing. In this paper, we present a new method which fancifully utilizes Biterm Topic Model (BTM) and Support Vector Machine (SVM). By using BTM, though the dimensionality of training data is reduced significantly, it is still able to keep rich semantic information for the sparse data. We then employ SVM on the generated topics or features. Experiments on 20 Newsgroups and Tencent microblog dataset demonstrate that our approach can achieve excellent classifier performance in terms of precision, recall and F1 measure. Furthermore, it is proved that the proposed method has high efficiency compared with the combination of Latent Dirichlet Allocation (LDA) and SVM. Our method enhances the previous work in this field and establishes the foundation for further studies.

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Acknowledgments

This work is supported by Natural Science Foundations of China (No. 61170192), National High-tech R&D Program of China (No. 2013AA013801), Fundamental Research Funds for the Central Universities (No. XDJK2016E064).

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Authors and Affiliations

  1. Faculty of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Guangjing Wang, Jie Zhang, Xiaobin Yang & Li Li

Authors
  1. Guangjing Wang
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  2. Jie Zhang
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  3. Xiaobin Yang
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  4. Li Li
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Corresponding author

Correspondence to Li Li .

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Editors and Affiliations

  1. Tsinghua University , Beijing, China

    Shaoxu Song

  2. Beihang University , Beijing, China

    Yongxin Tong

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Wang, G., Zhang, J., Yang, X., Li, L. (2016). A Hybrid Approach for Sparse Data Classification Based on Topic Model. In: Song, S., Tong, Y. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9998. Springer, Cham. https://doi.org/10.1007/978-3-319-47121-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-47121-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47120-4

  • Online ISBN: 978-3-319-47121-1

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