Abstract
In recent years, online reviews reflecting customers’ opinion play a significant role in the unprecedented success of online marketing system. Consumers can make justifiable judgement about the quality of products or services based on the large volume of user-generated reviews even when the provider is unknown. Therefore, this online review platform faces frequent abuse by fraudsters’ activities that often mislead potential customers and organizations thereby reshaping their businesses. Consequently, with immense technological advancement and diversity of products, the organizations are becoming competitors for each other, and hence, there is a growing tendency among merchants to hire professionals for writing deceptive reviews to promote their own products and defame others. Hence, trustworthiness of those reviewers and authenticity of their reviews are crucial from the perspective of e-commerce. This paper reviews several methodologies to identify spam or false reviews. We have also discussed different feature extraction techniques and parameters used in those algorithms.
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Acknowledgements
The research has been conducted under a research project titled “Review Spam Detection and Product Recommendation System using Machine Learning Techniques” sponsored by The Bhawanipur Education Society College. Authors are thankful to the Research and Publication Cell of the College for the Research Grant which provided the computational and other facilities.
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Halder, S., Dutta, S., Banerjee, P., Mukherjee, U., Mehta, A., Ganguli, R. (2021). A Survey on Online Spam Review Detection. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1300. Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_68
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DOI: https://doi.org/10.1007/978-981-33-4367-2_68
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