Abstract
The most important sources of information about the products or services are online reviews. People trust online review comments while purchasing electronics items, hotel booking, college admission, movies, etc. Sometimes it has been intentionally written by the fake reviewers for monetary gain, business rivalry, etc. Many times, these messages were found fake after the judgments. The fake reviews transmission has a significant social and economic impact on society. Hence, an accurate detection mechanism must be there to identify fake reviews. In this paper, the opinion spam detection mechanism is proposed using sentiment analysis (SA) for content-based applications. In this technique, the sentiment score of the sentences is computed. It is detected as fake or not fake, depending on the sentiment score of reviews. The work also proposed a Long Short-Term Memory (LSTM) based deep learning approach to identify the topic of fake reviews. Combining these two approaches provides a more accurate opinion spam detection rate compared to other existing models. On the benchmark “Deceptive Opinion Spam Corpus v1.4” dataset is used. Our model’s accuracy is 92.46% with 9.23% of the false acceptance rate and 5.50% of the false rejection rate.
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Singh, A., Chatterjee, K. (2022). A Comparative Approach for Opinion Spam Detection Using Sentiment Analysis. In: Rawat, S., Kumar, A., Kumar, P., Anguera, J. (eds) Proceedings of First International Conference on Computational Electronics for Wireless Communications. Lecture Notes in Networks and Systems, vol 329. Springer, Singapore. https://doi.org/10.1007/978-981-16-6246-1_43
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