Movie rating and review summarization in mobile environment

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 42, Issue 3, 2012, Pages 397-407

  Liu, Chien Liang ^a   Hsaio, Wen Hoar ^a   Lee, Chia Hoang ^a   Lu, Gen Chi ^b   Jou, Emery ^c  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b Hon Hai Precision Industry   (Taiwan)

^c INSTITUTE FOR INFORMATION INDUSTRY   (Taiwan)

Author keywords

Feature extraction; natural language processing (NLP); text analysis; text mining

Indexed keywords

FEATURE-BASED; LATENT SEMANTIC ANALYSIS; MOBILE ENVIRONMENTS; MOVIE RATINGS; NATURAL LANGUAGE PROCESSING; PRODUCT FEATURE; SYSTEM RESPONSE TIME; TEXT ANALYSIS; TEXT MINING;

COMPUTATIONAL LINGUISTICS; FEATURE EXTRACTION; MOTION PICTURES; RATING;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84860216754     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2011.2136334     Document Type: Review

References (26)

1. B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: Sentiment classification using machine learning techniques", in Proc. ACL-02 Conf. Empirical Methods Natural Lang. Process., 2002, pp. 79-86.
2. P. D. Turney, "Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews", in Proc. 40th Annu. Meeting Assoc. Comput. Linguist., 2002, pp. 417-424.
3. A. Esuli and F. Sebastiani, "Determining the semantic orientation of terms through gloss classification", in Proc. 14th ACM Int. Conf. Inf. Knowl. Manage., 2005, pp. 617-624. (Pubitemid 44022237)
4. S. H. Choi, Y.-S. Jeong, and M. K. Jeong, "A hybrid recommendation method with reduced data for large-scale application", IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 5, pp. 557-566, Sep. 2010.
5. T. Mullen and N. Collier, "Sentiment analysis using support vector machines with diverse information sources", in Proc. EMNLP, 2004, pp. 412-418.
6. M. Hu and B. Liu, "Mining and summarizing customer reviews", in Proc. 10th ACMSIGKDD Int. Conf. Knowl. Discov. DataMining, 2004, pp. 168-177. (Pubitemid 40114926)
7. V. Hatzivassiloglou and K. R. McKeown, "Predicting the semantic orientation of adjectives", in Proc. 8th Conf. Eur. Chap. Assoc. Comput. Linguist., Morristown, NJ: Assoc. Comput. Linguist., 1997, pp. 174-181.
8. A. Esuli and F. Sebastiani, "SENTIWORDNET: A publicly available lexical resource for opinion mining", in Proc. 5th Conf. Lang. Res. Eval., 2006, pp. 417-422.
9. K. Dave, S. Lawrence, and D. M. Pennock, "Mining the peanut gallery: opinion extraction and semantic classification of product reviews", in Proc. 12th Int. Conf. World Wide Web, New York: ACM, 2003, pp. 519-528.
10. V. N. Vapnik, The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 1995.
11. B. Pang and L. Lee, "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales", in Proc. 43rd Annu. Meet. Assoc. Comput. Linguist, Morristown, NJ: Assoc. Comput. Linguist., 2005, pp. 115-124.
12. A. B. Goldberg and X. Zhu, "Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization", in Proc. TextGraphs: First Workshop Graph Based Methods Nat. Lang. Process, Morristown, NJ: Assoc. Comput. Linguist., 2006, pp. 45-52.
13. B. Snyder and R. Barzilay, "Multiple aspect ranking using the good grief algorithm", in Proc. HLT-NAACL, 2007, pp. 300-307.
14. L. Zhuang, F. Jing, and X.-Y. Zhu, "Movie review mining and summarization", in Proc. 15th ACM Int. Conf. Inf. Knowl. Manage., 2006, pp. 43-50. (Pubitemid 47203550)
15. Y. Lu, C. Zhai, and N. Sundaresan, "Rated aspect summarization of short comments", in Proc. 18th Int. Conf. World Wide Web, New York: ACM, 2009, pp. 131-140.
16. T. Hofmann, J. Puzicha, and M. I. Jordan, "Learning from dyadic data", in Proc. Conf. Adv. Neural Inform. Process. Syst. II, Cambridge, MA: MIT Press, 1999, pp. 466-472.
17. T. K. Landauer, P. W. Foltz, and D. Laham, "Introduction to latent semantic analysis", Discourse Processes, vol. 25, pp. 259-284, 1998.
18. T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Norwell, MA: Kluwer, 2002.
19. C. Silva, U. Lotrič, B. Ribeiro, and A. Dobnikar, "Distributed text classification with an ensemble kernel-based learning approach", IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., vol. 40, no. 3, pp. 287-297, May 2010.
20. L. Rokach and O. Maimon, "Top-down induction of decision trees classifiers-A survey", IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 35, no. 4, pp. 476-487, Nov. 2005. (Pubitemid 41638167)
21. G. P. Zhang, "Neural networks for classification: A survey", IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 30, no. 4, pp. 451-462, Nov. 2000. (Pubitemid 32190902)
22. (2001). LIBSVM: A library for support vector machines [Online]. Available: http://www.csie.ntu.edu.tw/cjlin/libsvm.
23. T. Hofmann, "Unsupervised learning by probabilistic latent semantic analysis", Mach. Learn., vol. 42, no. 1/2, pp. 177-196, 2001. (Pubitemid 32872403)
24. A. P. Dempster, N. M. Laird, and D. B. Rubin. (1977). Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc., Series B [Online]. vol. 39, no. 1, pp. 1-38. Available: http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.133.4884.
25. C. D. Manning, P. Raghavan, and H. Schtze, Introduction to Information Retrieval. New York: Cambridge Univ. Press, 2008.
26. D. Ramage, P. Heymann, C. D. Manning, and H. Garcia-Molina, "Clustering the tagged web", in Proc. 2nd ACM Int. Conf. Web Search Data Mining, New York: ACM, 2009, pp. 54-63.