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Indexed by:期刊论文

Date of Publication:2016-10-01

Journal:INFORMATION PROCESSING LETTERS

Included Journals:SCIE、EI、Scopus

Volume:116

Issue:10

Page Number:623-627

ISSN No.:0020-0190

Key Words:Opinion mining; Feature extraction; Local context information; Global context information; Graph algorithms

Abstract: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.