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T-Bert: A Spam Review Detection Model Combining Group Intelligence and Personalized Sentiment Information

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Artificial Neural Networks and Machine Learning – ICANN 2021 (ICANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12895))

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Abstract

The content of online comments largely affects users’ willingness to purchase goods or services. Driven by interests, spam reviews continue to emerge to induce users maliciously. Most of the existing related work is based on the easy-camouflaged feature information, and the deep learning model is rarely used. The BERT model is prominent in various tasks in the NLP field, and whether it can be successfully applied to the spam review identification task has not been verified. In this paper, we propose a new research strategy for this task: the multi-dimensional representation combining group intelligence and users’ personalized sentiment information can more effectively detect spam reviews. Through fine-grained sentiment analysis of reviews based on product dimension and user dimension, we effectively acquire group intelligence and user personalized sentiment, respectively; Based on the ability of BERT to model the embedding of text context information, the semantic information is acquired. Finally, the three are combined based on Triple Network structure to detect spam reviews. We conduct a large number of experiments on three public datasets and the recall rate and F1 value both exceed the results of state-of-the-art works, which proves the feasibility and effectiveness of our proposed strategy, and verifies the modeling ability of the BERT in the task of detecting spam reviews.

Supported by the National Natural Science Foundation of China under Grant 61702091 and the Fundamental Research Funds for the Central Universities under Grant No 2572018BH06.

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Author information

Authors and Affiliations

  1. School of Information and Computer Engineering, Northeast Forestry University, Harbin, 150006, China

    Yue Shang & Meiling Liu

  2. Department of Computer Science, Harbin Institute of Technology, Harbin, 150001, China

    Tiejun Zhao

  3. Lieber Institute, Johns Hopkins University, Baltimore, MD, 21218, USA

    Jiyun Zhou

Authors
  1. Yue Shang
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  2. Meiling Liu
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  3. Tiejun Zhao
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  4. Jiyun Zhou
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Corresponding author

Correspondence to Meiling Liu .

Editor information

Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  3. University of Tübingen, Tübingen, Baden-Württemberg, Germany

    Sebastian Otte

  4. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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Shang, Y., Liu, M., Zhao, T., Zhou, J. (2021). T-Bert: A Spam Review Detection Model Combining Group Intelligence and Personalized Sentiment Information. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12895. Springer, Cham. https://doi.org/10.1007/978-3-030-86383-8_33

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  • DOI: https://doi.org/10.1007/978-3-030-86383-8_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86382-1

  • Online ISBN: 978-3-030-86383-8

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