An empirical study of sentiment analysis for chinese documents

Expert Systems with Applications

Volumn 34, Issue 4, 2008, Pages 2622-2629

  Tan, Songbo ^a   Zhang, Jin ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

Information retrieval; Machine learning; Sentiment analysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; INFORMATION RETRIEVAL; SUPPORT VECTOR MACHINES;

SENTIMENT ANALYSIS; SENTIMENT CLASSIFICATION; SENTIMENTAL TERMS SELECTION;

PATTERN MATCHING;

EID: 38649085269     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.05.028     Document Type: Article

References (25)

1. Chaovalit, Pimwadee, & Zhou, Lina (2005). Movie review mining: A comparison between supervised and unsupervised classification approaches. In IEEE proceedings of the 38th Hawaii international conference on system sciences, Big Island, Hawaii (pp. 1-9).
2. Galavotti, L., Sebastiani, F., & Simi, M. (2000). Feature selection and negative evidence in automated text categorization. In Proceedings of KDD.
3. Gamon, Michael (2004). Sentiment classification on customer feedback data: Noisy data,large feature vectors, and the role of linguistic analysis. In Proceedings the 20th international conference on computational linguistics.
4. Han E., and Karypis G. Centroid-based document classification analysis and experimental result. PKDD (2000)
5. Hatzivassiloglou, Vasileios, & McKeown, Kathleen (1997). Predicting the semantic orientation of adjectives. In Proceedings of the 35th ACL/8th EACL (pp. 174-181).
6. Joachims T. Text categorization with support vector machines: Learning with many relevant features. ECML (1998) 137-142
7. Kennedy, Alistair, & Inkpen, Diana (2005). Sentiment classification of movie and product reviews using contextual valence shifters. In Workshop on the analysis of informal and formal information exchange during negotiations (FINEXIN 2005).
8. McCallum A., and Nigam K. A comparison of event models for Naive Bayes text classification. AAAI/ICML-98 workshop on learning for text categorization [C] (1998), AAAI Press, Menlo Park, CA 41-48
9. Mullen T., and Collier N. Sentiment analysis using support vector machines with diverse information sources. In: Lin D., and Wu D. (Eds). Proceedings of EMNLP-2004, Barcelona, Spain, July 2004 (2004), Association for Computational Linguistics 412-418
10. Pang, Bo, Lee, Lillian, & Vaithyanathan, Shivakumar (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of EMNLP, 2002.
11. Stone P.J., Dunphy D.C., Smith M.S., Ogilvie D.M., and and associates. The general inquirer: A computer approach to content analysis (1966), The MIT Press
12. Turney, Peter D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the association for computational linguistics 40th anniversary meeting, New Brunswick, NJ.
13. Turney, D. Peter, & Littman, Michael L. (2002). Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical Report EGB-1094, National Research Council Canada.
14. Turney P.D., and Littman M.L. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS) 21 4 (2003) 315-346
15. van Mun, P. P. T. M. Text classification in information retrieval using winnow. http://citeseer.ist.psu.edu/cs.
16. van Rijsbergen C. Information retrieval (1979), Butterworth, London
17. Vapnik V.N. The nature of statistical learning theory (1995), Springer, New York
18. Whitelaw C., Garg N., and Argamon S. Using appraisal groups for sentiment analysis. CIKM (2005) 625-631
19. Yang Y. A study on thresholding strategies for text categorization. SIGIR (2001) 137-145
20. Yang Y., and Lin X. A re-examination of text categorization methods. SIGIR (1999) 42-49
21. Yang Y., and Pedersen J.O. A comparative study on feature selection in text categorization. ICML (1997) 412-420
22. Ye, Qiang, Lin, Bin, & Li, Yijun (2005). Sentiment classification for Chinese reviews: A comparison between SVM and semantic approaches. In The 4th international conference on machine learning and cybernetics ICMLC2005, June 2005.
23. Ye, Qiang, Shi, Wen, & Li, Yijun (2006). Sentiment classification for movie reviews in Chinese by improved semantic oriented approach. In Proceedings of HICSS-39 Hawaii international conference on system sciences, January 2006.
24. Zhang T. Regularized winnow methods. Advances in Neural Information Processing Systems 13 (2001) 703-709
25. Zhang, Huaping (2003). Chinese lexical analysis using hierarchical hidden Markov model. In Second SIGHAN workshop affiliated with 41th ACL, Sapporo Japan, July 2003 (pp. 63-70).