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Industrial Conference on Data Mining

ICDM 2018: Advances in Data Mining. Applications and Theoretical Aspects pp 119–134Cite as

Evolutionary DBN for the Customers’ Sentiment Classification with Incremental Rules

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10933))

Abstract

An increasing number of reviews from the customers have been available online. Thus, sentiment classification for such reviews has attracted more and more attention from the natural language processing (NLP) community. Related literature has shown that sentiment analysis can benefit from Deep Belief Networks (DBN). However, determining the structure of the deep network and improving its performance still remains an open question. In this paper, we propose a sophisticated algorithm based on fuzzy mathematics and genetic algorithm, called evolutionary fuzzy deep belief networks with incremental rules (EFDBNI). We evaluate our proposal using empirical data sets that are dedicated for sentiment classification. The results show that EFDBNI brings out significant improvement over existing methods.

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Acknowledgement

This work was supported by Beijing Natural Science Foundation P.R. China (4173072).

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Authors and Affiliations

  1. Beijing University of Technology, Beijing, 100022, People’s Republic of China

    Ping Yang, Dan Wang, Xiao-Lin Du & Meng Wang

Authors
  1. Ping Yang
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  2. Dan Wang
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  3. Xiao-Lin Du
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  4. Meng Wang
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Corresponding author

Correspondence to Dan Wang .

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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Yang, P., Wang, D., Du, XL., Wang, M. (2018). Evolutionary DBN for the Customers’ Sentiment Classification with Incremental Rules. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-95786-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95785-2

  • Online ISBN: 978-3-319-95786-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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