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New Technology of Library and Information Service  2014, Vol. 30 Issue (5): 74-82    DOI: 10.11925/infotech.1003-3513.2014.05.10
INFORMATION ANALYSIS AND RESEARCH Current Issue | Archive | Adv Search |
Research on Construction of Feature-Sentiment Ontology and Sentiment Analysis
Du Jiazhong1, Xu Jian1, Liu Ying2
1 School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China;
2 Sun Yat-Sen University Libraries, Guangzhou 510275, China
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[Objective] In a specific domain, sentiment analysis, mostly based on general lexicon, cannot identify the context-specific sentiment belonging to the domain. Also, the same word in the specific domain shows different polarities (positive, negative, neutral) when describing different properties. The objective of this paper is to solve the problems described above. [Methods] A sentiment analysis approach based on domain-oriented specific sentiment phrases is proposed. By developing feature-sentiment Ontology, general sentiment and specific sentiment can be divided during the process of sentiment analysis. [Results] The proposed method shows fairly better results of precision and recall in terms of phrase-level sentiment analysis. [Limitations] In order to get better analysis, the Ontology should cover the concepts in the related field as much as possible and should be well-built; the authors ignore the syntactic rules during the concept extraction and sentiment analysis, because the product comments are not normative; in the phase of sentiment analysis, the authors assume that the context like conjunction would not affect the polarity. [Conclusions] The new method not only makes improvement on sentiment analysis by solving the problem described above, but also proposes a new way for sentiment lexicon management.

Key wordsSentiment analysis      Domain sentiment phrase      Ontology      Online review     
Received: 31 December 2013      Published: 06 June 2014
:  G250  

Cite this article:

Du Jiazhong, Xu Jian, Liu Ying. Research on Construction of Feature-Sentiment Ontology and Sentiment Analysis. New Technology of Library and Information Service, 2014, 30(5): 74-82.

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[1] Pang B, Lee L. Opinion Mining and Sentiment Analysis [J]. Foundations and Trends in Information Retrieval, 2008, 2(1-2): 1-135.
[2] Tong R M. An Operational System for Detecting and Tracking Opinions in Online Discussion[C]. In: Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification. New York: ACM, 2001: 1-6.
[3] Kim S M, Hovy E. Determining the Sentiment of Opinions[C]. In: Proceedings of the 20th International Conference on Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2004.
[4] 张成功, 刘培玉, 朱振方, 等. 一种基于极性词典的情感分析方法[J].山东大学学报:理学版, 2012, 47(3): 47-50.(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.)
[5] Zhou L, Chaovalit P. Ontology‐Supported Polarity Mining [J]. Journal of the American Society for Information Science and Technology, 2008, 59(1): 98-110.
[6] Peñalver-Martínez I, Valencia-García R, García-Sánchez F. Ontology-Guided Approach to Feature-Based Opinion Mining [C]. In: Proceedings of the 16th International Conference on Natural Language Processing and Information Systems. Berlin, Heidelberg: Springer -Verlag, 2011: 193-200.
[7] 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.
[8] 李耀林. 面向评价对象的商品评论情感倾向性分析研究[D]. 杭州: 浙江工商大学, 2013. (Li Yaolin. Research on Sentiment Orientation Analysis of Product Comment in Evaluation Objects[D]. Hangzhou: Zhejiang Gongshang University, 2013.)
[9] 黄亿华. 基于情感评价单元的商品评论分析研究[D]. 南京: 南京大学, 2011. (Huang Yihua. Research on Product Reviews Analysis Based on Appraisal Expression [D]. Nanjing: Nanjing University, 2011.)
[10] 张清亮, 徐健. 网络情感词自动识别方法研究[J]. 现代图书情报技术, 2011(10): 24-28. (Zhang Qingliang, Xu Jian. Research on Automatic Extraction of Web Sentiment Words[J]. New Technology of Library and Information Service, 2011(10): 24-28.)
[11] Hu M, Liu B. Mining and Summarizing Customer Reviews[C]. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2004: 168-177.
[12] Salton G, McGill M J. Introduction to Modern Information Retrieval [M]. New York, NY, USA: McGraw-Hill Inc.,1986.
[13] Turney P. Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL [C]. In:Proceeding of the 12th European Conference on Machine Learning (ECML-2001). London: Springer, 2001: 491-502.
[14] [DB/OL]. [2013-06-20]. http://www.
[15] Zhang H P, Yu H K, Xiong D Y, et al. HHMM-based Chinese Lexical Analyzer ICTCLAS[C]. In: Proceedings of the 2nd SIGHAN Workshop on Chinese Language Processing-Volume 17. Stroudsburg, PA, USA: Association for Computational Lin-guistics, 2003: 184-187.
[16] HowNet[DB/OL]. [2013-06-20].
[17] Knublauch H, Fergerson R W, Noy N F, et al. The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications [C]. In: Proceedings of the Semantic Web-ISWC 2004. Berlin, Heidelberg: Springer, 2004: 229-243.
[18] Zhuang L, Jing F, Zhu X Y. Movie Review Mining and Summarization [C]. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management. New York:ACM, 2006: 43-50.
[19] Shi B, Chang K. Mining Chinese Reviews[C]. In: Proceedings of the 6th IEEE International Conference on Data Ming, Hong Kong, China. IEEE, 2006: 585-589.

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