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Spam review detection using LSTM autoencoder: an unsupervised approach

Author

Listed:
  • Sunil Saumya

    (Indian Institute of Information Technology Dharwad)

  • Jyoti Prakash Singh

    (National Institute of Technology Patna)

Abstract

The review of online products or services is becoming a major factor in the user’s purchasing decisions. The popularity and influence of online reviews attract spammers who intend to elevate their products or services by writing positive reviews for them and lowering the business of others by writing negative reviews. Traditionally, the spam review identification task is seen as a two-class classification problem. The classification approach requires a labelled dataset to train a model for the environment it is working on. The unavailability of the labelled dataset is a major limitation in the classification approach. To overcome the problem of the labelled dataset, we propose an unsupervised learning model combining long short-term memory (LSTM) networks and autoencoder (LSTM-autoencoder) to distinguish spam reviews from other real reviews. The said model is trained to learn the patterns of real review from the review’s textual details without any label. The experimental results show that our model is able to separate the real and spam review with good accuracy.

Suggested Citation

  • Sunil Saumya & Jyoti Prakash Singh, 2022. "Spam review detection using LSTM autoencoder: an unsupervised approach," Electronic Commerce Research, Springer, vol. 22(1), pages 113-133, March.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:1:d:10.1007_s10660-020-09413-4
    DOI: 10.1007/s10660-020-09413-4
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    References listed on IDEAS

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    1. Ajay Rastogi & Monica Mehrotra, 2017. "Opinion Spam Detection in Online Reviews," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1-38, December.
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    Cited by:

    1. Fadhila Lachekhab & Messouada Benzaoui & Sid Ahmed Tadjer & Abdelkrim Bensmaine & Hichem Hamma, 2024. "LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor," Energies, MDPI, vol. 17(10), pages 1-18, May.
    2. Shugang Li & Fang Liu & Yuqi Zhang & Boyi Zhu & He Zhu & Zhaoxu Yu, 2022. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review," Mathematics, MDPI, vol. 10(19), pages 1-26, September.
    3. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).

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