%0 Generic %1 Jitendra2017 %A Rout, Jitendra Kumar %A Dalmia, Anmol %A Choo, Kim-Kwang Raymond %A Bakshi, Sambit %A Jena, S. K. %D 2017 %J IEEE Access %K fake_reviews deceptive_review_detection semi-supervised_learning online_reviews posted_with_chatgpt %P 1319-1327 %R 10.1109/ACCESS.2017.2655032 %T Revisiting Semi-Supervised Learning for Online Deceptive Review Detection %U https://www.semanticscholar.org/paper/c6a281bb267f979a151c75f501df48b2872ecee9 %V 5 %X 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.

@JournalArticle{Jitendra2017, abstract = {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.}, added-at = {2024-01-25T23:09:51.000+0100}, author = {Rout, Jitendra Kumar and Dalmia, Anmol and Choo, Kim-Kwang Raymond and Bakshi, Sambit and Jena, S. K.}, biburl = {https://www.bibsonomy.org/bibtex/25fcddab42416b4ae81222db09957f5ea/mfhepp}, day = 18, description = {This paper explores the use of semi-supervised learning methods for detecting deceptive and fake online reviews, particularly in the context of hotel reviews. It addresses the economic impact of online opinion reviews on consumer decision-making and business profitability.}, doi = {10.1109/ACCESS.2017.2655032}, interhash = {3e3c0e7735c9822abd8649b83899d039}, intrahash = {5fcddab42416b4ae81222db09957f5ea}, journal = {IEEE Access}, keywords = {fake_reviews deceptive_review_detection semi-supervised_learning online_reviews posted_with_chatgpt}, month = {1}, pages = {1319-1327}, timestamp = {2024-01-25T23:09:51.000+0100}, title = {Revisiting Semi-Supervised Learning for Online Deceptive Review Detection}, url = {https://www.semanticscholar.org/paper/c6a281bb267f979a151c75f501df48b2872ecee9}, volume = 5, year = 2017 }