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China National Conference on Chinese Computational Linguistics

International Symposium on Natural Language Processing Based on Naturally Annotated Big Data

CCL 2015, NLP-NABD 2015: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data pp 393–404Cite as

Learning Document Representation for Deceptive Opinion Spam Detection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9427))

Abstract

Deceptive opinion spam in reviews of products or service is very harmful for customers in decision making. Existing approaches to detect deceptive spam are concern on feature designing. Hand-crafted features can show some linguistic phenomenon, but is time-consuming and can not reveal the connotative semantic meaning of the review. We present a neural network to learn document-level representation. In our model, we not only learn to represent each sentence but also represent the whole document of the review. We apply traditional convolutional neural network to represent the semantic meaning of sentences. We present two variant convolutional neural-network models to learn the document representation. The model taking sentence importance into consideration shows the better performance in deceptive spam detection which enhances the value of F1 by 5 %.

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Acknowledgments

This work was supported by the National High Technology Development 863 Program of China (NSFC) via grant 2015 AA015407, NSFC via grant 61133012 and NSFC via grant 61273321.

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Authors and Affiliations

  1. Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China

    Luyang Li, Wenjing Ren, Bing Qin & Ting Liu

Authors
  1. Luyang Li
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  2. Wenjing Ren
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  3. Bing Qin
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  4. Ting Liu
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Corresponding author

Correspondence to Bing Qin .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Tsinghua University, Beijing, China

    Zhiyuan Liu

  3. Soochow University, Suzhou, Jiangsu, China

    Min Zhang

  4. Tsinghua University, Beijing, China

    Yang Liu

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Li, L., Ren, W., Qin, B., Liu, T. (2015). Learning Document Representation for Deceptive Opinion Spam Detection. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-25816-4_32

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