Incorporating Users' Bias for Document-level Opinion Mining in Persian

Opinion mining or sentiment analysis is a field of study that aims to extract usefulknowledge like people's opinions, evaluations, and emotions expressed as textualcomments on the Web. Most existing Opinion mining methods have concentrated onEnglish and few studies considered the problem of Opinion mining in Persian.However, due to the lack of resources and tools for Persian text processing, opinionmining is more challenging in Persian. In this paper, a new method for document-levelopinion mining in Persian is presented. In the proposed method, each document is firstdecomposed into its sentences then, the sentiment score of each sentence is detected andfinally the obtained results are fused into an overall sentiment measure. In order to takeadvantage of significant knowledge that may be extracted from users’ commenthistories, the psychological theory of negativity bias is employed in the proposedmethod. According to this theory, different people respond differently to the sameconcept or event and this is a stable personal characteristic over time. Therefore, in theproposed system a bias measure is calculated for each user based on his/her history ofcomments and used to adjust the calculated sentiment of his/her new documents. Theperformance of the proposed method is assessed in classifying both the polarity andscore of online cell phone reviews. The results show the superiority of the proposedapproach compared to the state-of-the-art machine learning methods.

کلیدواژه ها:

Opinion mining ، web mining ، knowledge extraction ، information retrieval ، natural language processing