Automating Opinion Analysis in Film Reviews: The Case of Statistic Versus Linguistic Approach

作者: Damien Poirier , Cécile Bothorel , Émilie Guimier De Neef , Marc Boullé

DOI: 10.1007/978-94-007-1757-2_11

关键词:

摘要: Websites dedicated to collecting and disseminating opinions about goods, services, ideas,attract a diversity of comprising attitudes emotions. www.flixster.com is an example participative web site, where enthusiastic reviewers share their feelings/views on movies– usually expressing polar opinions. The web-sites contain substantial amount data which continually been updated.The contents such websites regarded as key source information by academic commercial researchers keen gauge this sample public opinion. challenge automatically extract the Our goal use reviews for building model can then be used predict user’s verdict movie. We explore two different methods extracting first, machine learning method that uses naive Bayesian classifier. second builds upon existing NLP techniques process build dictionaries: those dictionaries are determine polarity comment review. compare contrast relative merits with special reference movie review bases.

参考文章(39)
Kamal Nigam, Matthew Hurst, Towards a Robust Metric of Opinion ,(2004)
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)
Ron Kohavi, George John, Wrappers for feature selection Artificial Intelligence. ,(1997)
Marc Boullé, Compression-Based Averaging of Selective Naive Bayes Classifiers Journal of Machine Learning Research. ,vol. 8, pp. 1659- 1685 ,(2007)
Laurent Candillier, Frank Meyer, Marc Boullé, Comparing State-of-the-Art Collaborative Filtering Systems machine learning and data mining in pattern recognition. pp. 548- 562 ,(2007) , 10.1007/978-3-540-73499-4_41
William W. Cohen, Learning trees and rules with set-valued features national conference on artificial intelligence. pp. 709- 716 ,(1996)
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)
Minqing Hu, Bing Liu, Mining opinion features in customer reviews national conference on artificial intelligence. pp. 755- 760 ,(2004)
Chris T. Volinsky, Adrian E. Raftery, David Madigan, Jennifer A. Hoeting, Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors Statistical Science. ,vol. 14, pp. 382- 417 ,(1999) , 10.1214/SS/1009212519
Kevin Thompson, Pat Langley, and Wayne Iba, An analysis of Bayesian classifiers national conference on artificial intelligence. pp. 223- 228 ,(1992)