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DCWord: A Novel Deep Learning Approach to Deceptive Review Identification by Word Vectors

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Abstract

Due to the anonymous and free-for-all characteristics of online forums, it is very hard for human beings to differentiate deceptive reviews from truthful reviews. This paper proposes a deep learning approach for text representation called DCWord (Deep Context representation by Word vectors) to deceptive review identification. The basic idea is that since deceptive reviews and truthful reviews are composed by writers without and with real experience on using the online purchased goods or services, there should be different contextual information of words between them. Unlike state-of-the-art techniques in seeking best linguistic features for representation, we use word vectors to characterize contextual information of words in deceptive and truthful reviews automatically. The average-pooling strategy (called DCWord-A) and max-pooling strategy (called DCWord-M) are used to produce review vectors from word vectors. Experimental results on the Spam dataset and the Deception dataset demonstrate that the DCWord-M representation with LR (Logistic Regression) produces the best performances and outperforms state-of-the-art techniques on deceptive review identification. Moreover, the DCWord-M strategy outperforms the DCWord-A strategy in review representation for deceptive review identification. The outcome of this study provides potential implications for online review management and business intelligence of deceptive review identification.

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Acknowledgments

This research is supported in part by National Natural Science Foundation of China under Grant Nos. 71932002, 61379046, 91318302 and 61432001; the Innovation Fund Project of Xi’an Science and Technology Program (Special Series for Xi’an University under Grant No. 2016CXWL21). Also, the authors sincerely thank the referees for their much practical help to improve the quality of this paper.

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Authors and Affiliations

  1. Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, Beijing, 100124, China

    Wen Zhang, Qiang Wang & Jian Li

  2. School of Information Engineering, Xi’an University, Xi’an, 710065, China

    Xiangjun Li

  3. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, 923-1292, Japan

    Taketoshi Yoshida

Authors
  1. Wen Zhang
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  2. Qiang Wang
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  3. Xiangjun Li
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  4. Taketoshi Yoshida
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  5. Jian Li
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Corresponding author

Correspondence to Wen Zhang.

Additional information

Wen Zhang is a professor of College of Economics and Management at Beijing University of Technology (BJUT). He received his PhD degree in knowledge science from the Japan Advanced Institute of Science and Technology in 2009. His recent research interests include machine learning, data mining, and information systems.

Qiang Wang is a PhD candidate of College of Economics and Management at Beijing University of Technology (BJUT). He received his BS degree in marketing from Qufu Normal University in 2016. His research interest includes E-commerce big data analysis, data mining, and machine learning.

Xiangjun Li is a professor with School of Information Engineering, Xi’an University. She received her PhD from Xidian University in 2013. Her current research interest includes data mining, knowledge discovery, and machine learning.

Taketoshi Yoshida is a professor with School of Knowledge Science, Japan Advanced Institute of Science and Technology. He received his PhD degree in systems engineering from Case Western Reserve University in 1984. His current research interest includes knowledge management, knowledge discovery, and information systems.

Jian Li is a professor of College of Economics and Management at Beijing University of Technology (BJUT). He received his PhD degree from Chinese Academy of Sciences in 2007. His recent research interests include supply chain finance, blockchain technology, and emergency management.

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Zhang, W., Wang, Q., Li, X. et al. DCWord: A Novel Deep Learning Approach to Deceptive Review Identification by Word Vectors. J. Syst. Sci. Syst. Eng. 28, 731–746 (2019). https://doi.org/10.1007/s11518-019-5438-4

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  • DOI: https://doi.org/10.1007/s11518-019-5438-4

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