[1]HUANGFU Luwen,MAO Wenji.OCC-model-based text-emotion mining method[J].CAAI Transactions on Intelligent Systems,2017,12(5):645-652.[doi:10.11992/tis.201312032]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 5
Page number:
645-652
Column:
学术论文—智能系统
Public date:
2017-10-25
- Title:
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OCC-model-based text-emotion mining method
- Author(s):
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HUANGFU Luwen; MAO Wenji
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State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
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- Keywords:
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opinion mining; OCC emotion model; emotional dimension; emotion types; emotion dictionary; cognitive psychology; emotion mining; co-occurrence
- CLC:
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TP391
- DOI:
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10.11992/tis.201312032
- Abstract:
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Opinion mining, also called sentiment analysis, as one of the core research areas in the network-oriented social media analysis and mining domain, has important practical and research significance. Due to the weaknesses and limitations of traditional opinion mining methods, in this study, we designe and implemente an OCC emotion model-based opinion mining method for extracting emotion types from text. First, we adopte a statistical method to construct an emotion dictionary, based on candidate sets collected by the WordNet dictionary, as well as several syntactic dependent relationships and a small amount of annotated data. Next, we refine the constructed emotion-dimension dictionary to improve its quality by filtering out non-emotional words as well as emotional words that have conflicting syntactic or orientation. Lastly, we generate six main emotion types based on the obtained emotion-dimension dictionary combined with the corresponding relations between emotional dimensions and the different emotion types identified by the OCC model. Experimental results show that the proposed method has obvious advantages with respect to flexibility of usage, interpretability, and effectiveness.