Journal
IEEE ACCESS
Volume 5, Issue -, Pages 1319-1327Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2655032
Keywords
Online review spam; semi-supervised learning; unlabeled reviews; PU learning; Co-training; EM algorithm; label propagation and spreading
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Funding
- Information Security Education and Awareness Project (Phase II), Ministry of Electronics and Information Technology (MeitY), Government of India
- Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions Program, Department of Science and Technology, Government of India [ETI/359/2014]
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With more consumers using online opinion reviews to inform their service decision making, opinion reviews have an economical impact on the bottom line of businesses. Unsurprisingly, opportunistic individuals or groups have attempted to abuse or manipulate online opinion reviews (e.g., spam reviews) to make profits and so on, and that detecting deceptive and fake opinion reviews is a topic of ongoing research interest. In this paper, we explain how semi-supervised learning methods can be used to detect spam reviews, prior to demonstrating its utility using a data set of hotel reviews.
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