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Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining

  • S.I. : Emerging applications of Deep Learning and Spiking ANN
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Abstract

Fake consumer review detection has attracted much interest in recent years owing to the increasing number of Internet purchases. Existing approaches to detect fake consumer reviews use the review content, product and reviewer information and other features to detect fake reviews. However, as shown in recent studies, the semantic meaning of reviews might be particularly important for text classification. In addition, the emotions hidden in the reviews may represent another potential indicator of fake content. To improve the performance of fake review detection, here we propose two neural network models that integrate traditional bag-of-words as well as the word context and consumer emotions. Specifically, the models learn document-level representation by using three sets of features: (1) n-grams, (2) word embeddings and (3) various lexicon-based emotion indicators. Such a high-dimensional feature representation is used to classify fake reviews into four domains. To demonstrate the effectiveness of the presented detection systems, we compare their classification performance with several state-of-the-art methods for fake review detection. The proposed systems perform well on all datasets, irrespective of their sentiment polarity and product category.

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Notes

  1. See http://myleott.com/op-spam.html.

  2. See https://www.kaggle.com/lievgarcia/amazon-reviews.

  3. See http://jmcauley.ucsd.edu/data/amazon/.

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Acknowledgements

This article was supported by the scientific research project of the Czech Sciences Foundation Grant No: 19-15498S and by the Operational Program: Research and Innovation project “Fake news on the Internet—identification, content analysis, emotions”, co-funded by the European Regional Development Fund.

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  1. Faculty of Economics and Administration, Institute of System Engineering and Informatics, University of Pardubice, Studentská 84, 532 10, Pardubice, Czech Republic

    Petr Hajek & Aliaksandr Barushka

  2. Department of Computer Science, Constantine the Philosopher University in Nitra, 949 74, Nitra, Slovakia

    Michal Munk

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  1. Petr Hajek
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Hajek, P., Barushka, A. & Munk, M. Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Comput & Applic 32, 17259–17274 (2020). https://doi.org/10.1007/s00521-020-04757-2

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