Feature extraction in opinion mining through Persian reviews | ||
| Journal of AI and Data Mining | ||
| مقاله 6، دوره 3، شماره 2، مهر 2015، صفحه 169-179 اصل مقاله (914.42 K) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.5829/idosi.JAIDM.2015.03.02.06 | ||
| نویسندگان | ||
| E. Golpar-Rabooki1؛ S. Zarghamifar* 2؛ J. Rezaeenour3 | ||
| 1Department of Mathematics, University of Qom, Qom, Iran | ||
| 2Department of Computer Engineering, University of Qom, Qom, Iran | ||
| 3Department of Industrial Engineering, University of Qom, Qom, Iran | ||
| چکیده | ||
| Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which can play an important role in making major decisions in such area. In general, opinion mining extracts user reviews at three levels of document, sentence and feature. Opinion mining at the feature level is taken into consideration more than the other two levels due to orientation analysis of different aspects of an area. In this paper, two methods are introduced for feature extraction. The recommended methods consist of four main stages. At the first stage, opinion-mining lexicon for Persian is created. This lexicon is used to determine the orientation of users’ reviews. The second one is the preprocessing stage including unification of writing, tokenization, creating parts-of-speech tagging and syntactic dependency parsing for documents. The third stage involves the extraction of features using two methods including frequency-based feature extraction and association rule based feature extraction. In the fourth stage, the features and polarities of the word reviews extracted in the previous stage are modified and the final features' polarity is determined. To assess the suggested techniques, a set of user reviews in both scopes of university and cell phone areas were collected and the results of the two methods were compared. | ||
| کلیدواژهها | ||
| Opinion Mining؛ Feature extraction؛ Opinion-mining Lexicon؛ Corpus؛ Parts-of-speech Tagging؛ Syntactic Dependency Parsing | ||
|
آمار تعداد مشاهده مقاله: 2,981 تعداد دریافت فایل اصل مقاله: 3,913 |
||