Improving sentiment analysis in an online cancer survivor community using dynamic sentiment lexicon

Proceedings - 2013 International Conference on Social Intelligence and Technology, SOCIETY 2013

Volumn , Issue , 2013, Pages 109-113

  Ofek, Nir ^a,c   Caragea, Corneli ^b   Biyani, Prakhaa ^c   Rokach, Lior ^a,c   Mitra, Prasenjit ^c   Yen, John ^c   Portier, Kenneth ^d   Greer, Greta ^d  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b Center for Advanced Research and Technology   (United States)

^c PENNSYLVANIA STATE UNIVERSITY   (United States)

^d AMERICAN CANCER SOCIETY   (United States)

Author keywords

Abstract features; Dynamic sentiment lexicon; Sentiment analysis

Indexed keywords

ADDITIONAL EXPERIMENTS; AMERICAN CANCER SOCIETY; ATTRIBUTE VALUES; AUTOMATED APPROACH; DIMENSIONALITY REDUCTION; ONLINE HEALTH COMMUNITIES; SENTIMENT ANALYSIS; SENTIMENT LEXICONS;

DISEASES;

DATA MINING;

EID: 84881186112     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SOCIETY.2013.20     Document Type: Conference Paper

References (22)

1. K. Zickuhr, "Generations 2010, " Pew Internet, Tech. Rep., 2010.
2. B. Qiu, K. Zhao, P. Mitra, D. Wu, C. Caragea, J. Yen, G. E. Greer, K. Portier. (2011) Get Online Support, Feel Better-Sentiment Analysis and Dynamics in an Online Cancer Survivor Community. In: SocialCom 2011.
3. H. Minqing and L. Bing. Mining and Summarizing Customer Reviews. SIGKDD 2004, pages 168-177, 2004.
4. P. D. Turney. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Procs. of ACL'02, pages 417-424. 2002.
5. T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Human Langugae Technology Conference and Conference on EMNLP, pages 347-354. ACL, 2005.
6. P. D. Turney and M. L. Littman. Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst., 21(4):315-346, 2003.
7. J. Blitzer, M. Dredze, and F. Pereira. 2007. Biographies, Bollywood, Boom-boxes, and Blenders: Domain Adaptation for Sentiment Classification. ACL 2007.
8. L. D. Baker, and A. K. McCallum. 1998. Distributional clustering of words for text classification. In ACM SIGIR, 96-103. ACM Press.
9. C. Caragea, A. Silvescu, S. Kataria, D. Caragea, and P. Mitra. "Classifying Scientific Publications Using Abstract Features." In: SARA, Parador de Cardona, Spain, 2011.
10. T. Mullen and N. Collier, "Sentiment analysis using support vector machines with diverse information sources, " in Proceedings of the Conference on EMNLP, pp. 412-418, July 2004. (Poster paper).
11. K. Yang, N. Yu, and H. Zhang. WIDIT in TREC 2007. Blog Track: Combining Lexicon-Based Methods to Detect Opinionated Blogs. In Proceedings of TREC 2007.
12. S. R. Das and M. Y. Chen, "Yahoo! for Amazon: Sentiment extraction from small talk on the Web, " Management Science, vol. 53, pp. 1375-1388, 2007.
13. G. A. Miller. Wordnet: A lexical database for English. Communications of the ACM, (11):39-41, 1995. http://wordnet.princeton.edu/.
14. J. Kamps, M. Marx, R. J. Mokken, and M. De Rijke. 2004. Using WordNet to measure semantic orientation of adjectives. In: LREC 2004, Lisbon, PT.
15. S. Blair-Goldensohn, K. Hannan, R. McDonald, T. Neylon, G. A. Reis, and J. Reynar. Building a sentiment summarizer for local service reviews. In WWW Workshop on NLP in the Information, 2008.
16. A. Esuli and F. Sebastiani. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In Proc. of LREC 2006.
17. L. Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
18. J. J. Rodriguez, L. I. Kuncheva, C. J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630.
19. Y. Freund, R. E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 1996.
20. H. Witten and E. Frank.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 1999.
21. N. Landwehr, M. Hall, E. Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205.
22. C. Cortes and V. Vapnik, "Support-vector network, " Machine Learning, vol. 20, pp. 273-297, 1995.