Identifying evaluative sentences in online discussions

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2011, Pages 933-938

  Zhai, Zhongwu ^a   Liu, Bing ^b   Zhang, Lei ^b   Xu, Hua ^a   Jia, Peifa ^a  

^a TSINGHUA UNIVERSITY   (China)

^b University of Illinois at Chicago   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ONLINE DISCUSSIONS; OPINION MINING; PRODUCT REVIEWS; UNSUPERVISED APPROACHES;

ARTIFICIAL INTELLIGENCE;

EID: 80055025629     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Hassan A., V. Qazvinian and D. Radev. 2010. What's with the Attitude? Identifying Sentences with Attitude in Online Discussions. Proc. of EMNLP.
2. HowNet.http://www.keenage.com/html/e-bulletin-2007.htm.
3. Hu M. and B. Liu. 2004. Mining and Summarizing Customer Reviews. Proc. of KDD.
4. Kim S. and E. Hovy. 2006a. Identifying and Analyzing Judgment Opinions. Proc. of HLT-NAACL.
5. Kim S. and E. Hovy. 2006b. Automatic Identification of Pro and Con Reasons in Online Reviews. Proc. of COLING.
6. Kleinberg J. 1999. Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46(5). 46: 604-632.
7. Liu B. 2010. Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing N. Indurkhya and F. J. Damerau.
8. Pang B. and L. Lee. 2008. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval. 2: 1135.
9. Parrott W. 2001. Emotions in Social Psychology, Psyc. Press.
10. Qiu G., B. Liu, J. Bu and C. Chen. 2010. Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics.
11. Wiebe J. M. and E. Riloff. 2005. Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. Proc. of CICLing.
12. Wilson T., J. Wiebe and R. Hwa. 2004. Just How Mad Are You? Finding Strong and Weak Opinion Clauses. Proc. of AAAI.
13. Yu H. and V. Hatzivassiloglou. 2003. Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences. Proc. of EMNLP.