Extracting Opinion Features in Sentiment Patterns
Due to the increasing amount of opinions and reviews on the Internet, opinion mining has become a hot topic in data mining, in which extracting opinion features is a key step. Most of existing methods utilize a rule-based mechanism or statistics to extract opinion features, but they ignore the structure characteristics of reviews. The performance has hence not been promising. A new approach of OFESP (Opinion Feature Extraction based on Sentiment Patterns) is proposed in this paper, which takes into account the structure characteristics of reviews for higher values of precision and recall. With a selfconstructed database of sentiment patterns, OFESP matches each review sentence to obtain its features, and then filters redundant features regarding relevance of the domain, statistics and semantic similarity. Experimental studies on real-world data demonstrate that as compared with the traditional method based on a window mechanism, OFESP outperforms it on precision, recall and F-score. Meanwhile, in comparison to the approach based on syntactic analysis, OFESP performs better on recall and Fscore.
opinion mining pattern matching semantic similarity opinion feature.
Yongyong Zhai Yanxiang Chen Xuegang Hu Peipei Li Xindong Wu
School of Computer Science and Information Engineering,Hefei University of Technology,China,230009 School of Computer Science and Information Engineering,Hefei University of Technology,China,230009 D
国际会议
昆明
英文
115-119
2010-10-17（万方平台首次上网日期，不代表论文的发表时间）