Atefeh Mohammadi - Department of Computer Engineering, Yazd University, Yazd, Iran.
Mohammad-Reza Pajoohan - Department of Computer Engineering, Yazd University, Yazd, Iran.
Morteza Montazeri - Department of Computer Engineering, University of Isfahan, Isfahan, Iran.
MohammadAli Nematbakhsh - Department of Computer Engineering, University of Isfahan, Isfahan, Iran.

Recently, the approach towards mining various opinions on weblogs, forums and websites has gained attentions and interests of numerous researchers. In this regard, feature-based opinion mining has been extensively studied in English documents in order to identify implicit and explicit product features and relevant opinions. However, in case of texts written in Persian language, this task faces serious challenges. The objective of this research is to present an unsupervised method for feature-based opinion mining in Persian; an approach which does not require a labeled training dataset. The proposed method in this paper involves extracting explicit product features. Previous studies dealing with extraction of explicit features often focus on lexical roles of words; the approach which cannot be used in distinguishing between an adjective as a part of a noun or a sentiment word. In this study, in addition to lexical roles, syntactic roles are also considered to extract more relevant explicit features. The results demonstrate that the proposed method has got higher recall and precision values compared to prior studies.

Explicit Feature, Implicit Feature, Association Rules, Co-occurrence