A deceptive detection model based on topic, sentiment, and sentence structure information

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 50; no. 11; pp. 3868 - 3881
• Main Authors: Du, Xiaodong, Zhu, Ruiqi, Zhao, Fuqiang, Zhao, Fangzhou, Han, Ping, Zhu, Zhengyu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Computer Science; Deception; Dirichlet problem; Feature extraction; Linguistics; Machines; Manufacturing; Maximum likelihood estimates; Mechanical Engineering; Methods; Probability distribution; Processes; Support vector machines; Multiple-classifier; Feature extraction; Deceptive detection; Topic model

Deceptive reviews on Web are a common phenomenon and how to detect them has a very important impact on products, services, and even business policies. In order to filter out deceptive reviews more accurately, a new model called Sentence Joint Topic Sentiment Model (SJTSM) is presented in this paper,...