Study on the extraction of Chinese microblog subjective sentences based on lexicon and corpus

ZHU Hai-huan; YU Qing-song

Issue 4

Jul. 2014

Turn off MathJax

Article Contents

Article Navigation > Journal of East China Normal University (Natural Sciences) > 2014 > (4): 62-68, 87

ZHU Hai-huan, YU Qing-song. Study on the extraction of Chinese microblog subjective sentences based on lexicon and corpus[J]. Journal of East China Normal University (Natural Sciences), 2014, (4): 62-68, 87.

Citation:

ZHU Hai-huan, YU Qing-song. Study on the extraction of Chinese microblog subjective sentences based on lexicon and corpus[J]. Journal of East China Normal University (Natural Sciences), 2014, (4): 62-68, 87.

Citation:

PDF( 613 KB)

Study on the extraction of Chinese microblog subjective sentences based on lexicon and corpus

1.
Computer Center, East China Normal University, Shanghai 200062, China

Funds:

null

Received Date: 2013-07-01
Rev Recd Date: 2013-10-01
Publish Date: 2014-07-25

Abstract

Abstract

In this paper, we propose a new method for the extraction of Chinese microblog subjective sentence, which is based on a combination of lexicon and corpus. By determining whether the sentence contains emotional expressions, it can be classified as a subjective or objective sentence. Firstly, a highly credible sentiment lexicon was built based on the words whose emotional orientation is fixed from the existing sentiment dictionary. Based on the highly credible sentiment lexicon, sentiment expressions can be extracted with assurance of accuracy. Finally, a N-POSW model was proposed for the corpus-based learning method. Through the 2-POSW model, the remained sentiment expressions in the sentence can be extracted, thus guaranteeing the overall recall rate. Experimental results show that the F Value in this paper increases 7{\%} compared with the traditional method, which is based on the large-scale sentiment lexicon.
- sentiment lexicon,
- highly credible lexicon,
- N-POSW model,
- subjective sentence

FullText(HTML)

References(1)

References

[1]

{1} KIM S M, HOVY E. Automatic detection of opinion bearing words and sentences[C]//Companion Volume to the Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP). Berlin: Springer, 2005: 61-66.
{2} WIEBE J, WILSON T, BELL M. Identifying collocations for recognizing opinions[C]//Proceedings of the ACL'01 Workshop on Collocation: Computational Extraction, Analysis, and Exploitation. Toulouse, FR: ACL, 2001: 24-31.
{3} WIEBE J, WILSON T. Learning to disambiguate potentially subjective expressions[C]//Proceedings of the 6th conference on Natural language learning-Volume 20. Stroudsburg, PA: Association for Computational Linguistics, 2002: 1-7.
{4} WILSON T, WIEBE J, HWA R. Just how mad are you? Finding strong and weak opinion clauses[C]//Proceedings of the National Conference on Artificial Intelligence. Menlo Park, CA; MIT Press; 1999, 2004: 761-769.
{5} WILSON T, WIEBE J, HEA R. Recognizing strong and weak opinion clauses[J]. Computational Intelligence. 2006, 22(2): 73-99.
{6} PANG B, LEE L. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts[C]//Proceedings of the 42nd annual meeting on Association for Computational Linguistics. [S.l.]: Association for Computational Linguistics, 2004: 271-278.
{7} LONG J, MO Y. Target-dependent Twitter Sentiment Classification [C]//Proceeding of the 49th Annual meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2011: 151-160.
{8} 叶强, 张紫琼, 罗振雄. 面问互联网评论情感分析的中文主观性自动判别方法研究[J]. 信息系统学报, 2007, 1(1): 7-91.
{9} 张博. 基于~SVM~的中文观点句抽取[D]. 北京邮电大学, 2011.
{10} 杨武, 宋静静, 唐继强. 中文微博情感分析中主客观句分类方法[J]. 重庆理工大学学报: 自然科学. 2013, 27(1): 51-56.
{11} 董振东, 董强. 知网简介[DB/OL]. [2013-7-20]. http://www.keenage.com.
{12} 台湾大学NTUSD-简体中文情感极性词典[DB/OL]. [2013-7-20]. http://www.datatang.com/data/11837.
{13} ICTCLAS ICTLAS汉语分词系统[DB/OL]. [2014-06-10]. http://www.ictclas.org.
{14} 中文信息技术专业委员会. 中文微博情感分析评测[EB/OL]. [2013-7-20]. http://tcci.ccf.org.cn/conference/2012/pages/page04_eva.html.

Relative Articles

Supplements(0)

Cited By

Proportional views