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Spam review detection using LSTM autoencoder: an unsupervised approach

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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.

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Acknowledgements

The author would like to acknowledge the Ministry of Electronics and Information Technology (MeitY), Government of India for supporting the financial assistant during research work through “Visvesvaraya Ph.D. Scheme for Electronics and IT”.

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Authors and Affiliations

  1. Indian Institute of Information Technology Dharwad, Dharwad, India

    Sunil Saumya

  2. National Institute of Technology Patna, Patna, India

    Jyoti Prakash Singh

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  1. Sunil Saumya
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  2. Jyoti Prakash Singh
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Correspondence to Sunil Saumya.

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Sunil Saumya was formerly at National Institute of Technology Patna.

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Saumya, S., Singh, J.P. Spam review detection using LSTM autoencoder: an unsupervised approach. Electron Commer Res 22, 113–133 (2022). https://doi.org/10.1007/s10660-020-09413-4

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