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New Technology of Library and Information Service  2007, Vol. 2 Issue (4): 43-47    DOI: 10.11925/infotech.1003-3513.2007.04.11
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An Analysis and Comparison of Three Methods for Document Semantic Orientation Recognition
Ma Haibing1   Liu Yongdan1   Wang Lancheng1   Li Ronglu2
1(Shanghai Branch, Nanjing Political  Institute, Shanghai 200433, China)
2(Autodesk Incorporation, Shanghai 200001, China)
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Abstract  

This paper investigates and implements three methods for document semantic orientation recognition: one is based on the weighted emotional words; another is based on the analysis of the semantic pattern; and the third is based on text classification. The first method is try to use semantic orientation of feature words. The second method is to simplify the structure of the natural language syntax, in order to acquire the appropriate size patterns with semantics tendency. The third method is based on the direct use of traditional text classification method. Through experiments and the application in an analysis system of net-mediated public opinions, shortages and advantages of the three methods are proposed.
Key words： Natural language process      Text classification      Semantic orientation recognition     
Received: 06 March 2007      Published: 25 April 2007
: 

TP391

 		 
Corresponding Authors: Ma Haibing     E-mail: martin0721@163.com
About author:: Ma Haibing,Liu Yongdan,Wang Lancheng,Li Ronglu
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Cite this article:

Ma Haibing,Liu Yongdan,Wang Lancheng,Li Ronglu . An Analysis and Comparison of Three Methods for Document Semantic Orientation Recognition. New Technology of Library and Information Service, 2007, 2(4): 43-47.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2007.04.11     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2007/V2/I4/43

1Jaap Kamps, Maarten Marx, Robert J. Mokken, Maarten de Rijke. Using WordNet to Measure Semantic Orientations of Adjectives. In: Proceedings of the fourth international conference on Language Resources and Evaluation, 2004，IV:1115-1118
2Turney Peter. Thumbs Up or Thumbs Down ? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 2002: 417 -424
3Turney, Peter, Littman Michael. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 2003, 21(4): 315 - 346
4朱嫣岚,闵锦,周雅倩等. 基于HowNet 的词汇语义倾向计算. 中文信息学报,2006, 20(1): 14-20
5Yi J,Nasukawa T,Bunescu R,et al. Sentiment Analyzer: Extracting Sentiments about a Given Topic Using Natural Language Processing Techniques, In : Proceedings of the Third IEEE International Conference on Data Mining, 2003
6Dave K,Lawrence S,Pennock D. Mining the Peanut Gallery:Opinion Extraction and Semantic Classification of Product Reviews. In: Proceedings of the 22th International World Wide Web Conference, Budapest, Hungary, 2003
7Beineke P,Hastie T，Vaithyanathan S. The Sentimental Factor:Improving Review Classification Via Human-Provided Information. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, 2004
8刘永丹，曾海泉，李荣陆等基于语义分析的倾向性文本过滤.通信学报, 2004, 25(7):78-85
9Bo Pang , Lillian Lee , Shivakumar Vaithyanathan. Thumbs up? Sentiment Classification Using machine Learning Techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing. 2002：79-86
10Bo Pang, Lillian Lee. Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales. In: Proceedings of the Association for Computational Linguistics (ACL 2005), 2005: 115-124
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