论文类型：期刊论文
发表刊物：INFORMATION PROCESSING LETTERS
收录刊物：Scopus、EI、SCIE
卷号：116
期号：10
页面范围：623-627
ISSN号：0020-0190
关键字：Opinion mining; Feature extraction; Local context information; Global context information; Graph algorithms
摘要：Product feature (feature in brief) extraction is one of important tasks in opinion mining as it enables an opinion mining system to provide feature level opinions. Most existing feature extraction methods use only local context information (LCI) in a clause or a sentence (such as co-occurrence or dependency relation) for extraction. But global context information (GCI) is also helpful. In this paper, we propose a combined approach, which integrates LCI and GCI to extract and rank features based on feature score and frequency. Experimental evaluation shows that the combined approach does a good job. It outperforms the baseline extraction methods individually. (C) 2016 Elsevier B.V. All rights reserved.