Movie Rating and Review Summarization in Mobile Environment

In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.

[1]  Kathleen R. McKeown,et al.  Predicting the semantic orientation of adjectives , 1997 .

[2]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss classification , 2005, CIKM '05.

[3]  Xiaoyan Zhu,et al.  Movie review mining and summarization , 2006, CIKM '06.

[4]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[5]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[6]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[7]  Hector Garcia-Molina,et al.  Clustering the tagged web , 2009, WSDM '09.

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[11]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[12]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[13]  Thomas Hofmann,et al.  Learning from Dyadic Data , 1998, NIPS.

[14]  Xiaojin Zhu,et al.  Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization , 2006 .

[15]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[16]  Lior Rokach,et al.  Top-down induction of decision trees classifiers - a survey , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[18]  Regina Barzilay,et al.  Multiple Aspect Ranking Using the Good Grief Algorithm , 2007, NAACL.

[19]  Bernardete Ribeiro,et al.  Distributed Text Classification With an Ensemble Kernel-Based Learning Approach , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[20]  Myong Kee Jeong,et al.  A Hybrid Recommendation Method with Reduced Data for Large-Scale Application , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[21]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[22]  Yue Lu,et al.  Rated aspect summarization of short comments , 2009, WWW '09.

[23]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[24]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[25]  Nigel Collier,et al.  Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.

[26]  Thorsten Joachims,et al.  Learning to classify text using support vector machines - methods, theory and algorithms , 2002, The Kluwer international series in engineering and computer science.