A hierarchical approach to mood classification in blogs

Natural Language Engineering

Volumn 18, Issue 1, 2012, Pages 61-81

  Keshtkar, Fazel ^a   Inkpen, Diana ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BLOG POSTINGS; DATA SETS; HIERARCHICAL APPROACH; ORIENTATION FEATURES; STANDARD MACHINES;

ERROR ANALYSIS;

INTERNET;

EID: 84155172557     PISSN: 13513249     EISSN: 14698110     Source Type: Journal    
DOI: 10.1017/S1351324911000118     Document Type: Article

References (53)

1. Alm, C. O., Roth, D., and Sproat, R. 2005. Emotions from text: Lachine learning for text-based emotion prediction. In Proceedings of HLT/EMNLP 2005, pp. 579-86, Vancouver, British Columbia, Canada.
2. Aman, S., and Szpakowicz., S. 2007. Identifying expressions of emotion in text. In Proceedings of Text, Speech and Dialog (TSD 2007), pp. 196-205, Prague, Czech Republic.
3. Andreevskaia, A., and Bergler, S. 2008. When specialists and generalists work together: overcoming domain dependence in sentiment tagging. In Proceedings of ACL/HLT 2008, Columbus, OH, USA.
4. Banea, C., Mihalcea R., Wiebe, J., and Hassan, S. 2008. Multilingual subjectivity analysis using machine translation. In Proceedings of EMNLP 2008, Waikiki, Honolulu, HI, USA.
5. Blitzer, J., Dredze, M., and Pereira, F. 2007. Biographies, Bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In Proceedings of ACL 2007, Prague, Czech Republic.
6. Bradley, M., and Lang, P. 1999. Affective Norms for English Words. University of Florida, Gainesville, FL, USA.
7. Burges, C. J. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 3(2).
8. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37-46.
9. Dekel, O., Keshet, J., and Singer, Y. 2004. Large margin hierarchical classification. In Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada.
10. Devitt, A., and Ahmad, K. 2007. Sentiment polarity identification in financial news: A cohesion-based approach. In Proceedings of the ACL 2007, Prague, Czech Republic.
11. Ekman, P. 1992. An argument for basic emotions. Cognition and Emotion 6: 169-200.
12. Hastie, T., and Tibshirani, R. 1998. Classification by pairwise coupling. The Annals of Statistics 26(2): 451-471.
13. Hiroshi, K., Tetsuya, N., and Hideo, W. 2004. Deeper sentiment analysis using machine translation technology. In Proceedings of the COLING 2004, University of Geneva, Switzerland.
14. Holzman, L., and Pottenger, W. 2003. Classification of emotions in internet chat: An application of machine learning using speech phonemes. Technical Report LU-CSE-03-002, Lehigh University.
15. Izard, C. E. 1971. The Face of Emotion. New York: Appleton-Century- Crofts.
16. Joachims, T. 1998. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of ECML, pp. 137-42, Chemnitz, Germany.
17. Jung, Y., Park, H., and Myaeng, S. 2006. A hybrid mood classification approach for blog text. LNCS 4099: 1099-103. (Pubitemid 44531918)
18. Keerthi, S., Shevade, S., Bhattacharyya, C., and Murthy, K. 2001. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation 13(3): 637-49. (Pubitemid 33595014)
19. Kennedy, A., and Inkpen, D. 2006. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22(2): 110-25. (Pubitemid 43787303)
20. Kim, S.-M., and Hovy, E. 2004. Determining the sentiment of opinions. In Proceedings of COLING 2004, University of Geneva, Switzerland.
21. Kiritchenko, S., Matwin, S., Nock, R., and Famili, F. 2006. Learning in the presence of class hierarchies. In Proceedings of Learning@Snowbird 2006, Snowbird UT, Princeton, NJ.
22. Koppel, M., Argamon, A., and Shimoni, A. 2002. Automatically categorizing written texts by author gender. Literary and Linguistic Computing 17(4): 401-12.
23. Koppel, M., and Schler, J. 2004. Authorship verification as a one-class classification problem. In Proceedings of the International Conference on Machine Learning (ICML 2004), Banff, Alberta, Canada.
24. Li, J., and Sun, M. 2007. Experimental study on sentiment classification of Chinese review using machine learning techniques. In Proceedings of IEEE-NLPKE 2007, Beijing, China.
25. Liu, H., Lieberman, H., and Selker, H. 2003. A model of textual affect sensing using real-world knowledge. In Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI 2003), pp. 125-32, Miami, FL, USA.
26. Liu, H., and Singh, P. 2004. Conceptnet -a practical commonsense reasoning tool-kit. BT Technology Journal 22(4): 211-26.
27. McDonald, R., Hannan, K., Neylon, T., Wells, M., and Reynar, J. 2007. Structured models for fine-to-coarse sentiment analysis. In Proceedings of ACL 2007, Prague, Czech Republic.
28. Mihalcea, R., and Liu, H. 2006. A corpus-based approach to finding happiness. In Proceedings of the AAAI Spring Symposium on Computational Approaches to Weblogs, Stanford, CA.
29. Mishne, G. 2005. Experiments with mood classification in blog posts. In Proceedings of ACM SIGIR 2005, Salvador, Brazil.
30. Mullen, T., and Collier, N. 2004. Sentiment polarity identification in financial news: A cohesionbased approach. In Proceedings of EMNLP 2004, Barcelona, Spain.
31. Neviarouskaya, A., Prendinger, H., and Ishizuka, M. 2007. Analysis of affect expressed through the evolving language of online communication. In Proceedings of the International Conference on Intelligent User Interfaces (IUI 2007), pp. 278-81, Honolulu, HI. (Pubitemid 47464016)
32. Pang, B., and Lee, L. 2008. Opinion minding and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2): 1-135.
33. Passonneau, R. J. 2006. Measuring agreement on set-valued items (MASI) for semantic and pragmatic annotation. In Proceeding of the 5th International Conference on Language Resources and Evaluation (LREC 2006), Genoa, Italy.
34. Platt, J. 1998. Fast training of support vector machines using sequential minimal optimization. In B. Schoelkopf, C. Burges, and A. Smola (eds.), Advances in Kernel Methods, Support Vector Learning. MIT Press, Cambridge, MA, USA.
35. Quirk, R., Greenbaum, S., Leech, G., and Svartvik, J. 1985. A Comprehensive Grammar of the English Language. New York: Longman.
36. Read, J. 2004. Recognizing Affect in Text Using Pointwise Mutual Information. Master's thesis, Brighton, UK: University of Sussex.
37. Read, J. 2005. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of ACL 2005, University of Michigan, Ann Arbor, MI.
38. Rubin, V., Stanton, J., and Liddy, E. 2004. Discerning emotions in texts. In Proceedings of AAAI EAAT 2004, Stanford University, Stanford, CA.
39. Schmid, H. 1994. Probabilistic part-of-speech tagging using decision trees. In Proceedings of the International Conference on New Methods in Language Processing, Manchester, UK.
40. Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys 34(1): 1-47.
41. Stone, P. J., Dexter, D., Smith, C., Marshall, S., and Ogilvie, D. M. 2007. The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge, MA, USA.
42. Strapparava, C., and Mihalcea, R. 2007. SemEval-2007 Task 14: Affective text. In Proceedings of the 4th International Workshop on the Semantic Evaluations (SemEval 2007), Prague, Czech Republic, June 2007.
43. Strapparava, C., and Mihalcea, R. 2008. Learning to identify emotions in text. In Proceedings of the ACM symposium on Applied Computing (SAC 2008), Sackville, New Brunswick, Canada, August 2008.
44. Tsou, B. K. Y., Yuen, R. W. M., Kwong, O. Y., La, T. B. Y., and Wong, W. L. 2005. Polarity classification of celebrity coverage in the Chinese press. In Proceedings of International Conference on Intelligence Analysis, McLean, VA, USA.
45. Turney, P., and Littman, M. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL 2002, Philadelphia, PA.
46. Turney, P., and Littman, M. 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM (TOIS) 21(4), pp. 315-346.
47. Vapnik, V. 1996. The Nature of Statistical Learning Theory. New York: Springer.
48. Wan, X. 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of EMNLP 2008, Honolulu, HI.
49. Wang, K., Zhou, S., and Liew, S. 1999. Building hierarchical classifiers using class proximities. In Proceedings of VLDB 1999 , pp. 363-74, Edinburgh, Scotland, UK.
50. Wiebe, J., Wilson, T., and Cardie, C. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3): 165-210. (Pubitemid 43320361)
51. Wilson, T., Wiebe, J., and Hoffmann, P. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of HLT/EMNLP 2005, Vancouver, British Columbia, Canada.
52. Witten, I., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. San Francisco: Morgan Kaufmann.
53. Ye, Q., Shi, W., and Li, Y. 2006. Sentiment classification for movie reviews in Chinese by improved semantic oriented approach. In Proceedings of 39th Hawaii International Conference on System Sciences 2006, Hawaii, Kauai, USA.