Aspect-based sentiment analysis of movie reviews on discussion boards

Journal of Information Science

Volumn 36, Issue 6, 2010, Pages 823-848

  Thet, Tun Thura ^a   Na, Jin Cheon ^a   Khoo, Christopher S G ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

discussion board; opinion mining; sentiment analysis

Indexed keywords

DEPENDENCY STRUCTURES; DISCUSSION BOARDS; FINE-GRAINED ANALYSIS; LINGUISTIC APPROACH; OPINION MINING; SENTIMENT ANALYSIS; SENTIMENT STRENGTHS;

DATA MINING;

MOTION PICTURES;

EID: 78650156237     PISSN: 01655515     EISSN: 17416485     Source Type: Journal    
DOI: 10.1177/0165551510388123     Document Type: Article

References (49)

1. M. Chiu, Towards a hypermedia-enabled and web-based data analysis framework, Journal of Information Science 30 (1) (2004) 60-72.
2. Encyclopedia of Data Warehousing and Mining (Information Science Reference, Hershey, 2008 ).
3. R. Quirk, S. Greenbaum, G. Leech and J. Svartvik, A Comprehensive Grammar of the English Language ( Longman, London, 1985 ).
4. P. Chaovalit and L. Zhou, Movie review mining: a comparison between supervised and unsupervised classification approaches, Proceedings of the 38th Annual Hawaii International Conference on System Sciences ( 2005 ).
5. P.D. Turney, Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL (2002) 417-434.
6. A. Esuli and F. Sebastiani, Determining term subjectivity and term orientation for opinion mining, Proceedings of the European Chapter of the Association for Computational Linguistics (2006) 193-200.
7. T. Wilson, J. Wiebe and P. Hoffmann, Recognizing contextual polarity in phrase-level sentiment analysis, Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (2005) 347-354.
8. J. Wiebe, Tracking point of view in narrative, Computational Linguistics 20 (2) (1994) 233-287.
9. P.J. Stone, D.C. Dunphy, M.S. Smith and D.M. Ogilvie, General Inquirer: a Computer Approach to Content Analysis ( The MIT Press, Cambridge, MA, 1966 ).
10. V. Hatzivassiloglou and K.R. McKeown, Predicting the semantic orientation of adjectives, Proceedings of the 35th Annual Meeting of the ACL and the 8th Conference of the European Chapter of the ACL (1997) 174-181.
11. J. Wiebe, Learning subjective adjectives from corpora, Proceedings of the 17th National Conference on Artificial Intelligence (2000) 735-740.
12. E. Riloff, J. Wiebe and T. Wilson, Learning subjective nouns using extraction pattern bootstrapping, Proceeding of the 7th Conference on Natural Language Learning (2003) 25-32.
13. E. Riloff and J. Wiebe, Learning extraction patterns for subjective expressions, Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (2003) 105-112.
14. G. Qiu, B. Liu, J. Bu and C. Chen, Expanding domain sentiment lexicon through double propagation, Proceedings of the 21st International Joint Conference on Artificial Intelligence (Morgan Kaufmann, San Francisco, 2009) 1199-1204.
15. K. Dave, S. Lawrence and D.M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, Proceedings of the 12th International Conference on World Wide Web (2003) 519-528.
16. T. Mullen and N. Collier, Sentiment analysis using support vector machines with diverse information sources, Proceedings of the 9th Conference on Empirical Methods in Natural Language Processing (2004) 412-418.
17. B. Pang and L. Lee, Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales, Proceedings of the Association for Computational Linguistics (2005) 115-124.
18. B. Pang, L. Lee and S. Vaithyanathan, Thumbs up? Sentiment classification using machine-learning techniques, Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (2002) 79-86.
19. C. Whitelaw, N. Garg and S. Argamon, Using appraisal groups for sentiment analysis, Proceedings of the 14th ACM Conference on Information and Knowledge Management (2005) 625-631.
20. J.-C. Na and T.T. Thet, Effectiveness of web search results for genre and sentiment classification, Journal of Information Science 35 (6) (2009) 709-727.
21. L. Zhou and P. Chaovalit, Ontology-supported polarity mining, Journal of the American Society for Information Science and Technology 59 (1) (2008). 98-110.
22. L.-W. Ku and H.-H. Chen, Mining opinions from the Web: beyond relevance retrieval, Journal of the American Society for Information Science and Technology 58 (12) (2007). 1838-1850. (Pubitemid 47573438)
23. T. Joachims, Text categorization with support vector machines: learning with many relevant features, Proceedings of the 10th European Conference on Machine-learning (1998) 137-142.
24. J.R. Martin and P.R.R. White, The Language of Evaluation: Appraisal in English ( Palgrave Macmillan, London, 2005 ).
25. J.-C. Na, T.T. Thet and C.S.G. Khoo, Comparing sentiment expression in movie reviews from four online genres, Online Information Review 34 (2) (2010) 317-338.
26. S. Blair-Goldensohn, K. Hannan, R. McDonald, T. Neylon, G.A. Reis and J. Reynar, Building a sentiment summarizer for local service reviews, Proceedings of WWW 2008 Workshop: NLP Challenges in the Information Explosion Era ( 2008 ).
27. X. Ding, B. Liu and P.S. Yu, A holistic lexicon-based approach to opinion mining, Proceedings of the International Conference on Web Search and Web Data Mining (ACM, New York, 2008) 231-240.
28. M. Hu and B. Liu, Mining and summarizing customer reviews, Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004) 168-177.
29. M. Ganapathibhotla and B. Liu, Mining opinions in comparative sentences, Proceedings of the International Conference on Computational Linguistics (2008) 241-248.
30. S.-M. Kim and E. Hovy, Automatic detection of opinion bearing words and sentences, Proceedings of the International Joint Conference on Natural Language Processing (2005) 61-66.
31. T. Miyoshi and Y. Nakagami, Sentiment classification of customer reviews on electric products, Proceedings of International Conference on Systems, Man and Cybernetics (2007) 2028-2033.
32. M.A.M. Shaikh, H. Prendinger and M. Ishizuka, Sentiment assessment of text by analyzing linguistic features and contextual valence assignment, Applied Artificial Intelligence 22 (6) (2008) 558-601.
33. J. Yi, T. Nasukawa, R. Bunescu and W. Niblack, Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques, Proceedings of the 3rd IEEE International Conference on Data Mining (2003) 427-434.
34. C. Zhang, D. Zeng, J. Li, F.-Y. Wang and W. Zuo, Sentiment analysis of Chinese documents: from sentence to document level, Journal of the American Society for Information Science and Technology 60 (12) (2009) 2474-2487.
35. O. Vechtomova, Facet-based opinion retrieval from blogs, Information Processing and Management 46 (1) (2010) 71-88.
36. A.L. Berger, V.J. Della Pietra and S.A. Della Pietra, A maximum entropy approach to natural language processing, Computational Linguistics 22 (1) (1996) 39-71.
37. J. Wiebe and E. Riloff, Creating subjective and objective sentence classifiers from unannotated texts, Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics (2005) 475-486.
38. T. Wilson, J. Wiebe and R. Hwa, Recognizing strong and weak opinion clauses, Computational Intelligence 22 (2) (2006) 73-99. (Pubitemid 43787301)
39. R.E. Schapire and Y. Singer, BoosTexter: A boosting-based system for text categorization, Machine Learning 39 (2 / 3) (2000) 135-168. (Pubitemid 30594821)
40. W. Cohen, Learning trees and rules with set-valued features, Proceedings of the 13th National Conference on Artificial Intelligence/8th Innovative Applications of Artificial Intelligence Conference (AAAI/IAAI) Volume 1 (1996) 709-716.
41. K. Toutanova and C.D. Manning, Enriching the knowledge sources used in a maximum entropy part-of-speech tagger, Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000) (2000) 63-70.
42. T.M. Mitchell, Machine Learning ( McGraw Hill, New York, 1997 ).
43. S.E. Robertson and K. Sparck Jones, Relevance weighting of search terms, Journal of the American Society for Information Science 27 (3) (1976) 129-146.
44. Y. Yang and J.O. Pedersen, A comparative study on feature selection in text categorization, Proceedings of the Fourteenth International Conference on Machine Learning (ICML '97) (1997) 412-420.
45. M.-C. de Marneffe, B. MacCartney and C.D. Manning, Generating typed dependency parses from phrase structure parses, Proceedings of the 5th International Conference on Language Resources and Evaluation ( 2006 ).
46. M.-C. de Marneffe and C.D. Manning, Stanford typed dependencies manual (2010). Available at: http://nlp.standford.edu/software/dependencies-manual.pdf (accessed 10 October 2010 ).
47. T. Byrt, How good is that agreement ? Epidemiology 7 (5) (1996) 561.
48. J. Neter, W. Wasserman and G.A. Whitmore, Applied Statistics ( Allyn and Bacon, Boston, MA, 1992 ).
49. N. Jindal and B. Liu, Opinion Spam and Analysis, Proceedings of the International Conference on Web Search and Web Data Mining (WSDM'08) (Palo Alto, California, USA, 2008) 219-229.