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