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Weakly Supervised Feature Compression Based Topic Model for Sentiment Classification

  • Conference paper
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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

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Abstract

Sentiment classification aims to use automatic tools to explore the subjective information like opinions and attitudes from user comments. Most of existing methods are centered on the semantic relationships and the extraction of syntactic feature, while the document topic feature is ignored. In this paper, a weakly supervised hierarchical model called external knowledge-based Latent Dirichlet Allocation (ELDA) is proposed to extract document topic feature. First of all, we take advantage of ELDA to compress document feature and increase the polarity weight of document topic feature. And then, we train a classifier based on the topic feature using SVM. Experiment results on one English dataset and one Chinese dataset show that our method can outperform the state-of-the-art models by at least \(4\%\) in terms of accuracy.

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Notes

  1. 1.

    http://tcci.ccf.org.cn/conference/2014/.

  2. 2.

    http://www.datatang.com.

  3. 3.

    https://github.com/fxsjy/jieba.

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Acknowledgement

This work was supported by Natural Science Foundation of China (No. 61170192). Li L. is the corresponding author for the paper.

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Authors and Affiliations

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

    Yan Hu, Xiaofei Xu & Li Li

Authors
  1. Yan Hu
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  2. Xiaofei Xu
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  3. Li Li
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Corresponding author

Correspondence to Li Li .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Hu, Y., Xu, X., Li, L. (2017). Weakly Supervised Feature Compression Based Topic Model for Sentiment Classification. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

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