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The development of an automatic way to extract user opinions about products,movies, and foods from online social network (OSN) interactions is among themain interests of sentiment analysis and opinion mining studies. Existingapproaches in the sentiment analysis domain mostly do not discriminate thesentences of different types of users, even though some users are alwaysnegative and some are always positive. Thus, finding a way to identify thesetwo types of user is significant because their attitudes can change theanalysis of user reviews of businesses and products. Due to the complexity ofnatural language processing, pure text mining methods may lead tomisunderstandings about the exact nature of the sentiments expressed in reviewtext. In this study, we propose a neural network classifier to predict thepresence of biased users on the basis of users' psychological behaviors. Theidentification of the psychological behaviors of users allows us to find overlypositive and overly negative users and to categorize these users' attitudesregardless of the content of their review texts. The experiment resultindicates that the biased users can be predicted based on user behavior at anaccuracy rate of 89%, 67% and 81% for three different datasets.

Identifying Biased Users in Online Social Networks to Enhance the Accuracy of Sentiment Analysis: A User Behavior-Based Approach