[Objective] This paper studies the relationship between the product attributes and the emotional attitudes of consumers, aiming to optimize the sentiment analysis on consumer reviews. [Methods] First, we constructed the product domain ontology to extract the needed attributes. Then, we built the product attribute hierarchy model, which combined the collocation weight of emotional words with attribute words to identify implicit attributes. Third, we created a dictionary to calculate the emotional orientation of product attributes at all levels for the sentiment analysis. [Results] We examined the proposed model with online reviews of smart phones and found it improved the accuracy of emotion classification. [Limitations] The construction of ontology needs to be further improved. [Conclusions] The proposed method could effectively identify the logical relationship among attributes, which improve the performance of sentiment analysis in real world cases.
何有世, 何述芳. 基于领域本体的产品网络口碑信息多层次细粒度情感挖掘*[J]. 数据分析与知识发现, 2018, 2(8): 60-68.
He Youshi,He Shufang. Sentiment Mining of Online Product Reviews Based on Domain Ontology. Data Analysis and Knowledge Discovery, 2018, 2(8): 60-68.
(Yin Pei, Wang Hongwei.Sentiment Classification for Chinese Online Reviews at Product Feature Level Through Domain Ontology Method[J]. Journal of Systems and Management, 2016, 25(1): 103-114.)
[3]
Yu H, Hatzivassiloglou V.Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences[C]//Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing. 2003: 129-136.
[4]
Pang B, Lee L.Opinion Mining and Sentiment Analysis[J]. Foundations and Trends in Information Retrieval, 2008, 2(1-2): 1-135.
doi: 10.1561/1500000011
[5]
Turney P D.Thumbs up or Thumbs down?: Semantic Orientation Applied to Unsupervised Classification of Reviews[C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2002: 417-424.
[6]
He R, Gonzalez H.Numerical Synthesis of Pontryagin Optimal Control Minimizers Using Sampling-Based Methods[C]//Proceedings of the IEEE 56th Annual Conference on Decision and Control (CDC). Melbourne, Australia: IEEE CDC, 2017:733-738.
[7]
Meena A, Prabhakar T V.Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis[C]// Proceedings of the European Conference on Information Retrieval. 2007: 573-580.
(Zhang Chenggong, Liu Peiyu, Zhu Zhenfang, et al.A Sentiment Analysis Method Based on a Polarity Lexion[J]. Journal of Shandong University: Natural Science, 2012, 47(3): 47-50.)
[9]
Fu X, Liu G, Guo Y, et al.Multi-aspect Sentiment Analysis for Chinese Online Social Reviews Based on Topic Modeling and HowNet Lexicon[J]. Knowledge Based Systems, 2013, 37: 186-195.
doi: 10.1016/j.knosys.2012.08.003
[10]
Kim S M, Hovy E. Extracting Opinions, Opinion Holders,Topics Expressed in Online News Media Text[C]// Proceedings of the Workshop on Sentiment & Subjectivity in Text at the International Conference on Computational Linguistics/the Annual Meeting of the Association for Computational Linguistics Sentiment and Subject. 2006: 101-108.
[11]
Hai Z, Chang K, Kim J.Implicit Feature Identification via Co-occurrence Association Rule Mining[C]//Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics. Berlin: Springer-Verlag, 2011: 393-404.
(Zhu Yanlan, Min Jin, Zhou Yaqian, et al.Semantic Orientation Computing Based on HowNet[J]. Journal of Chinese Information Processing, 2006, 20(1): 14-20.)
doi: 10.3969/j.issn.1003-0077.2006.01.003
[13]
Xu H, Zhang F, Wang W.Implicit Feature Identification in Chinese Reviews Using Explicit Topic Mining Model[J]. Knowledge Based Systems. 2015, 76: 166-175.
doi: 10.1016/j.knosys.2014.12.012
[14]
Carenini G, Ng R T, Zwart E.Extracting Knowledge from Evaluative Text[C]//Proceedings of the 3rd International Conference on Knowledge Capture. Edmonton: ACM, 2005: 11-18.
[15]
Yu J X, Zha Z J, Wang M, et al.Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews[C]// Proceedings of 2011 Conference on Empirical Methods in Natural Language Processing. Edinburgh: ACL, 2011: 140-150.
[16]
Yin P, Wang H, Guo K.Feature-Opinion Pair Identification of Product Reviews in Chinese: A Domain Ontology Modeling Method[J]. New Review of Hypermedia and Multimedia, 2013, 19(1): 3-24.
doi: 10.1080/13614568.2013.766266
(Tang Xiaobo, Lan Yuting.Sentiment Analysis of Microblog Product Reviews Based on Feature Ontology[J]. Library and Information Service, 2016, 60(16): 121-127.)
(Du Jiazhong, Xu Jian, Liu Ying.Research on Construction of Feature-Sentiment Ontology and Sentiment Analysis[J]. New Technology of Library and Information Service, 2014(5): 74-82.)
(Li Jinhai, He Youshi, Ma Yunlei, et al.Building Dynamic User Preference Model Based on Information Mining of Online Reviews[J]. Journal of Intelligence, 2016, 35(9): 192-198.)
[20]
Gruber T R.Toward Principles for the Design of Ontologies Used for Knowledge Sharing[J]. International Journal of Human-Computer Studies, 1995, 43(5-6): 907-928.
doi: 10.1006/ijhc.1995.1081
(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.)
(Yang Yanxia.Tourism Network Comments Sentiment Analysis and Pre-warning System Based on Ontology[J]. Computer and Digital Engineering, 2016, 44(4): 649-652.)
doi: 10.3969/j.issn.1672-9722.2016.04.020
(Zhao Zhibin, Liu Huan, Yao Lan, et al.Research on Dimension Mining and Sentiment Analysis for Chinese Product Comments[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(3): 341-349.)