RLOSD: Representation learning based opinion spam detection
Zeinab Sedighi, Hossein Ebrahimpour-Komleh, Ayoub Bagheri · 2017
Nowadays, by vastly increasing in online reviews, harmful influence of spam reviews on decision making causes irrecoverable outcomes for both customers and organizations. Existing methods investigate for a way to contradistinction between spam and non-spam reviews. Most algorithms focus on feature engineering approaches to expose an accommodation of data representation. In this paper we propose a decision tree-based method to reveal deceptive reviews from trustworthy ones. We use unsupervised representation learning along with traditional feature selection methods to extract appropriate features and evaluate them with a decision tree. Our model takes data correlation into consideration to opt suitable features. The result shows the better performance in detecting opinion spam, comparing most common methods in this area.