[Objective] This study proposed a confidence ranking model to extract product feature and user opinion from the Chinese online reviews. [Methods] Examining the semantic and association relations between candidate words, we built the confidence ranking model based on the improved HITS algorithm, and then retrieved the feature and opinion words. [Results] Compared with the reference model, our method showed better recall and precision rates while extracting the feature and opinion words from the Chinese corpus. [Limitations] Only extracted the explicit feature and opinion words, and did not try to identify and extract the implicit ones. [Conclusions] We could effectively extract the feature and opinion words using their mutual reinforcement and semantic relations. Filtering method of the semantic polarity could also improve the precision of the extracted opinion words.
孟园, 王洪伟. 中文评论产品特征与观点抽取方法研究*[J]. 现代图书情报技术, 2016, 32(2): 16-24.
Yuan Meng, Hongwei Wang. Extracting Product Feature and User Opinion from Chinese Reviews. New Technology of Library and Information Service, 2016, 32(2): 16-24.
(Wang Yong, Zhang Qin, Yang Xiaojie.Research on the Method of Extracting Features from Chinese Product Reviews on the Internet[J]. New Technology of Library and Information Service, 2013(12): 70-73.)
[2]
Zhang L, Liu B, Lim S H, et al.Extracting and Ranking Product Features in Opinion Documents [C]. In: Proceedings of the 23rd International Conference on Computational Lingusitics (COLING), Beijing, China. Stroudsburg, PA, USA: ACL, 2010: 1462-1470.
(Xi Yahui.Extracting Product Features and Opinions from Product Reviews[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(3): 326-336.)
[4]
Jin W, Ho H H, Srihari R K.A Novel Lexicalized HMM-based Learning Framework for Web Opinion Mining [C]. In: Proceedings of the 26th Annual International Conference on Machine Learning (ICML), Montreal, Canada. New York, NY, USA: ACM, 2009: 465-472.
[5]
Li F T, Han C, Huang M L, et al.Structure-aware Review Mining and Summarization [C]. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING), Beijing, China. Stroudsburg, PA, USA: ACL, 2010: 653-661.
[6]
Wu Y B, Zhang Q, Huang X J, et al.Phrase Dependency Parsing for Opinion Mining [C]. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore. Morristown, NJ, USA: ACL, 2009: 1533-1541.
[7]
Titov I, McDonald R. Modeling Online Reviews with Multi-grain Topic Models [C]. In: Proceedings of the 17th International Conference on World Wide Web (WWW), Beijing, China. New York, NY, USA: ACM, 2008: 111-120.
[8]
Zhao W X, Jiang J, Yan H F, et al.Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid [C]. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), Massachusetts, USA. Stroudsburg, PA, USA: ACL, 2010: 56-65.
[9]
Hu M Q, Liu B.Mining and Summarizing Customer Reviews[C]. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, USA. New York, NY, USA: ACM, 2004: 168-177.
[10]
Aravindan S, Ekbal A.Feature Extraction and Opinion Mining in Online Product Reviews [C]. In: Proceedings of the 2014 International Conference on Information Technology (ICIT), Bhubaneswar, India. New York, NY, USA: IEEE, 2014: 94-99.
[11]
Qiu G, Liu B, Bu J J, et al.Opinion Word Expansion and Target Extraction Through Double Propagation[J]. Computational Linguistics, 2011, 37(1): 9-27.
[12]
Hai Z, Chang K Y, Cong G.An Association-Based Unified Framework for Mining Features and Opinion Words[J]. ACM Transaction on Intelligent Systems and Technology, 2015, 6(2): 2601-2626.
[13]
Liu K, Xu L H, Zhao J.Extracting Opinion Targets and Opinon Words from Online Reviews with Graph Co-ranking [C]. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Ligustics (ACL), Baltimore, USA. Stroudsburg, PA, USA: ACL, 2014: 314-324.
(Yin Pei, Wang Hongwei, Guo Kaiqiang.Feature- opinion Pair Identification in Chinese Online Reviews Based on Domain Ontology Modeling Method[J]. Systems Engineering, 2013, 31(1): 68-77.)