Simultaneously Identifying Opinion Targets and Opinion-bearing Words Based on Multi-features in Chinese Micro-blog Texts
Quanchao
Liu, Heyan Huang and Chong Feng
Department
of Computer Science and Technology, Beijing Institute of Technology, China
Abstract: We propose to simultaneously identify opinion targets and opinion-bearing words based on multi-features in Chinese micro-blog texts, i.e. to identify opinion-bearing words by means of opinion-bearing words dictionary and to identify opinion targets by considering multi-features between opinion targets and opinion-bearing words, and then we take a future step to optimize forwarding-based opinion target identification. We decompose our task into four phases: 1. Construct opinion-bearing words dictionary and identify opinion-bearing word in a sentence from Chinese micro-blog; 2. Design multiple features related to opinion target identification, containing Token, Part-Of-Speech (POS), Word Distance(WD), Direct Dependency Relation (DDR) and SRL; 3. Design three kinds of different feature templates to identify feature-opinion pairs <opinion target, opinion-bearing word> in Chinese micro-blog texts; 4. Combining forwarding relation between individual micro-blogs, we solve the problem of identifying opinion target in short micro-blog. The experiments with Natural Language Processing (NLP) and Chinese Computing (CC) 2012 and 2013’s labeled data show that our approach provides better performance than the baselines and most systems reported at NLP and CC 2012 and 2013.
Keywords: Opinion
mining, opinion target identification, micro-blog, feature-opinion pairs,
sentiment polarity.
Received January 14, 2015; accepted July 12, 2015
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