本文介绍了如何识别汉语语句主题和主题与情感描述项之间的关系以及如何计算主题的语义倾向(极性)。我们利用领域本体来抽取语句主题以及它的属性,然后在句法分析的基础上,识别主题和情感描述项之间的关系,从而最终决定语句中每个主题的极性。实验结果显示,与手工标注的语料作为金标准进行比较,用于识别主题和主题极性的改进后的SBV极性传递算法的F度量达到了72.41%。它比原来的SBV极性传递算法和VOB极性传递算法的F度量分别提高了7.6%和2.09%。因此,所建议的改进的SBV极性传递算法是合理和有效的。
Abstract
This paper presents how to identify the topics as well as the relations bewteen the topics and the sentimental descriptive terms in a Chinese sentence, and how to compute the sentiment orientation (polarity) of the topics. We extract the topics and their attributes from a sentence with the help of a domain ontology, then identify the relations between the topics and sentimental descriptive terms based on parsing results, and finally determine the polarity of each topic in the sentence. The experiment has shown that the F-measure of the improved SBV polarity transfer algorithm for identifying topics and the polarity reaches 72.41% as compared with the manual annotation corpus which serves as a golden standard. It is increased by 7.6% and 2.09% than the F-measure of the original SBV and VOB polarity transfer algorithm respectively. Therefore, the proposed improved SBV polarity transfer algorithm is reasonable and effective.
关键词
计算机应用 /
中文信息处理 /
意见挖掘 /
主题 /
语义倾向
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Key words
computer application /
Chinese information processing /
opinion mining /
topic /
semantic orientation
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参考文献
[1] S.-M. Kim and E. Hovy. Determining the Sentiment of Opinions [A]. In: Proceedings of COLING-04, the Conference on Computational Linguistics (COLING-2004) [C]. Geneva, Switzerland: 2004. 1367-1373.
[2] J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack. Sentiment Analyzer: Extracting Sentiments about a Given Topic Using Natural Language Processing Techniques [A]. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM-2003) [C]. Melbourne, USA: 2003. 427-434.
[3] M. Hu and B. Liu. Mining Opinion Features in Customer Reviews [A]. In: Proceedings of Nineteeth National Conference on Artificial Intellgience (AAAI-2004) [C]. San Jose, USA: 2004. 755-760.
[4] A.-M. Popescu and O. Etzioni. Extracting Product Features and Opinions from Reviews [A]. In: Proceedings of the Human Language Technology Conference/Conference on Empirical Methods in Natural Language Processing (HLT-EMNLP-05) [C]. Vancouver, Canada: 2005. 339-346.
[5] X. Cheng. Automatic Topic Term Detection and Sentiment Classification for Opinion Mining [D]. Master Thesis. Saarbr cken, Germany: The University of Saarland, 2007.
[6] R. Yuan et al. Morpheme-based Derivation of Bipolar Semantic Orientation of Chinese Words [A]. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING-2004) [C]. Geneva, Switzerland: 2004. 1008-1014.
[7] P. D. Turney and M. L. Littman. Measuring Praise and Criticism: Inference of Semantic Orientation from Association [J]. ACM Transactions on Information Systems, 2003, 21(4): 315-346.
[8] 朱嫣岚, 等. 基于HowNet的词汇语义倾向计算 [J].中文信息学报,2006, 20(1): 14-20.
[9] T. Nasukawa and J. Yi. Sentiment Analysis: Capturing Favorability using Natural Language Processing [A]. In: Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP 2003) [C]. Sanibel, USA: 2003. 70-77.
[10] B. Tsou et al. Polarity Classification of Celebrity Coverage in the Chinese Press [A]. In: Proceedings of the International Conference on Intelligence Analysis [C]. McLean, USA: 2005.
[11] 徐琳宏,林鸿飞,杨志豪. 基于语义理解的文本倾向性识别机制 [J]. 中文信息学报,2007, 21(1): 96-100.
[12] T. Wilson et al. OpinionFinder: A System for Subjectivity Analysis [A]. In: Proceedings of HLT/EMNLP 2005 Demonstration Abstracts [C]. Vancouver, Canada: 2005. 34-35.
[13] J. Yi and W. Niblack. Sentiment Mining in WebFountain [A]. In: Proceedings of the 21st International Conference on Data Engineering (ICDE 2005) [C]. Tokyo, Japan: IEEE Computer Society Press, 2005. 1073-1083.
[14] 姚天昉, 等. 一个用于汉语汽车评论的意见挖掘系统 [A]. 中文信息处理前沿进展-中国中文信息学会二十五周年学术会议论文集 [C]. 北京: 清华大学出版社, 2006, 260-281.
[15] 哈尔滨工业大学信息检索研究室. 中文依存句法分析概况介绍 [EB/OL].http://ir.hit.edu.cn/phpwebsite/index.php?module=pagemaster&PAGE user op=view page&PAGE id=147&MMN position=52:48, 2006.
[16] P. J. Stone, D. C. Dunphy, M. S. Smith, and D. M. Ogilvie. The General Inquirer: A Computer Approach to Content Analysis [M]. Cambridge, MA, USA: MIT Press. 1966.
[17] Z. Dong and Q. Dong. HowNet [EB/OL]. http://www.keenage.com/zhiwang/e zhiwang. html, 2003.
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