Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales

作者: Bo Pang , Lillian Lee

DOI: 10.3115/1219840.1219855

关键词:

摘要: We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must …

参考文章(26)
James P. Callan, Kevyn Collins-Thompson, A Language Modeling Approach to Predicting Reading Difficulty. north american chapter of the association for computational linguistics. pp. 193- 200 ,(2004)
Peter McCullagh, Regression Models for Ordinal Data Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 42, pp. 109- 127 ,(1980) , 10.1111/J.2517-6161.1980.TB01109.X
John Lafferty, Xiaojin Zhu, Ronald Rosenfeld, Semi-supervised learning with graphs Carnegie Mellon University. ,(2005)
Hiroshi Ishikawa, Davi Geiger, Occlusions, Discontinuities, and Epipolar Lines in Stereo european conference on computer vision. pp. 232- 248 ,(1998) , 10.1007/BFB0055670
Rebecca Hwa, Janyce Wiebe, Theresa Wilson, Just how mad are you? finding strong and weak opinion clauses national conference on artificial intelligence. pp. 761- 767 ,(2004)
Thorsten Joachims, Making large scale SVM learning practical Technical reports. ,(1999) , 10.17877/DE290R-14262
Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal, Locally Weighted Learning Artificial Intelligence Review. ,vol. 11, pp. 11- 73 ,(1997) , 10.1023/A:1006559212014
Eric Horvitz, David Hovel, Andy Jacobs, Attention-sensitive alerting uncertainty in artificial intelligence. pp. 305- 313 ,(1999)
Alex J. Smola, Bernhard Schölkopf, A tutorial on support vector regression Statistics and Computing. ,vol. 14, pp. 199- 222 ,(2004) , 10.1023/B:STCO.0000035301.49549.88
Robert E. Schapire, Yoram Singer, BoosTexter: A Boosting-based Systemfor Text Categorization Machine Learning. ,vol. 39, pp. 135- 168 ,(2000) , 10.1023/A:1007649029923