Combining local and global information for product feature extraction in opinion documents

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.