Abstract
It is hard to exaggerate how crucial user reviews are in figuring out an organization’s e-commerce income. Before purchasing any goods or service, online customers rely on product and service reviews. As a result, firms must consider how reliable Internet reviews are because they might directly affect their reputation and bottom line. Because of this, some businesses pay spammers to post false reviews. These false reviews profit from consumer buying choices. As a result, during the past twelve years, there has been substantial study into techniques for spotting false reviews. However, there is still a need for a survey that can analyze and summarize the diverse techniques. In this paper, we are going to put the SVM and Naive Bayesian machine learning model system into place that can spot false reviews with an accuracy of 0.801 and 0.687.
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Narayana Royal, M., Reddy, R.P.K., Sangathya, G.S., Sai Madesh Pretam, B., Kaliappan, J., Suganthan, C. (2023). Detection of Fake Reviews on Products Using Machine Learning. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2022). Lecture Notes in Networks and Systems, vol 615. Springer, Singapore. https://doi.org/10.1007/978-981-19-9304-6_54
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DOI: https://doi.org/10.1007/978-981-19-9304-6_54
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