Just how mad are you? finding strong and weak opinion clauses

作者: Rebecca Hwa , Janyce Wiebe , Theresa Wilson

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摘要: There has been a recent swell of interest in the automatic identification and extraction opinions emotions text. In this paper, we present first experimental results classifying strength other types subjectivity deeply nested clauses. We use wide range features, including new syntactic features developed for opinion recognition. 10-fold cross-validation experiments using support vector regression, achieve improvements mean-squared error over baseline ranging from 57% to 64%.

参考文章(28)
Janyce Wiebe, Theresa Wilson, Annotating Opinions in the World Press annual meeting of the special interest group on discourse and dialogue. pp. 13- 22 ,(2003)
Janyce Marbury Wiebe, Recognizing subjective sentences: a computational investigation of narrative text State University of New York at Buffalo. ,(1990)
J. Wiebe, Identifying Collocations for Recognizing Opinions meeting of the association for computational linguistics. ,(2001)
William W. Cohen, Learning trees and rules with set-valued features national conference on artificial intelligence. pp. 709- 716 ,(1996)
Janyce Wiebe, Learning Subjective Adjectives from Corpora national conference on artificial intelligence. pp. 735- 740 ,(2000)
Thorsten Joachims, Making large scale SVM learning practical Technical reports. ,(1999) , 10.17877/DE290R-14262
Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima, Mining product reputations on the Web Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02. pp. 341- 349 ,(2002) , 10.1145/775047.775098
Michael Collins, Three generative, lexicalised models for statistical parsing Proceedings of the 35th annual meeting on Association for Computational Linguistics -. pp. 16- 23 ,(1997) , 10.3115/976909.979620