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A hierarchical approach to mood classification in blogs

Published online by Cambridge University Press:  11 March 2011

FAZEL KESHTKAR  and
DIANA INKPEN
FAZEL KESHTKAR
Affiliation:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada e-mail: [email protected], [email protected]
DIANA INKPEN
Affiliation:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada e-mail: [email protected], [email protected]
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Abstract

In this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.

Type
Articles
Information
Natural Language Engineering , Volume 18 , Issue 1 , January 2012 , pp. 61 - 81
Copyright
Copyright © Cambridge University Press 2011

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References

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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Bradley, M., and Lang, P. 1999. Affective Norms for English Words. University of Florida, Gainesville, FL, USA.Google Scholar
Burges, C. J. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 3 (2).Google Scholar
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37–46.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
Ekman, P. 1992. An argument for basic emotions. Cognition and Emotion 6: 169–200.CrossRefGoogle Scholar
Hastie, T., and Tibshirani, R. 1998. Classification by pairwise coupling. The Annals of Statistics 26 (2): 451–471.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
Izard, C. E. 1971. The Face of Emotion. New York: Appleton-Century-Crofts.Google Scholar
Joachims, T. 1998. Text categorization with support vector machines: learning with many relevant features. In Proceedings of ECML, pp. 137–42, Chemnitz, Germany.Google Scholar
Jung, Y., Park, H., and Myaeng, S. 2006. A hybrid mood classification approach for blog text. LNCS 4099: 1099–103.Google Scholar
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.CrossRefGoogle Scholar
Kennedy, A., and Inkpen, D. 2006. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22 (2): 110–25.CrossRefGoogle Scholar
Kim, S.-M., and Hovy, E. 2004. Determining the sentiment of opinions. In Proceedings of COLING 2004, University of Geneva, Switzerland.Google Scholar
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.Google Scholar
Koppel, M., Argamon, A., and Shimoni, A. 2002. Automatically categorizing written texts by author gender. Literary and Linguistic Computing 17 (4): 401–12.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Liu, H., and Singh, P. 2004. Conceptnet - a practical commonsense reasoning tool-kit. BT Technology Journal 22 (4): 211–26.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
Mishne, G. 2005. Experiments with mood classification in blog posts. In Proceedings of ACM SIGIR 2005, Salvador, Brazil.Google Scholar
Mullen, T., and Collier, N. 2004. Sentiment polarity identification in financial news: a cohesion-based approach. In Proceedings of EMNLP 2004, Barcelona, Spain.Google Scholar
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.Google Scholar
Pang, B., and Lee, L. 2008. Opinion minding and sentiment analysis. Foundations and Trends in Information Retrieval 2 (1–2): 1–135.CrossRefGoogle Scholar
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.Google Scholar
Platt, J. 1998. Fast training of support vector machines using sequential minimal optimization. In Schoelkopf, B., Burges, C., and Smola, A. (eds.), Advances in Kernel Methods, Support Vector Learning. MIT Press, Cambridge, MA, USA.Google Scholar
Quirk, R., Greenbaum, S., Leech, G., and Svartvik, J. 1985. A Comprehensive Grammar of the English Language. New York: Longman.Google Scholar
Read, J. 2004. Recognizing Affect in Text Using Pointwise Mutual Information. Master's thesis, Brighton, UK: University of Sussex.Google Scholar
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.Google Scholar
Rubin, V., Stanton, J., and Liddy, E. 2004. Discerning emotions in texts. In Proceedings of AAAI EAAT 2004, Stanford University, Stanford, CA.Google Scholar
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.Google Scholar
Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys 34 (1): 1–47.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Turney, P., and Littman, M. 2003. Measuring praise and criticism: inference of semantic orientation from association. ACM (TOIS) 21 (4), pp. 315–346.CrossRefGoogle Scholar
Vapnik, V. 1996. The Nature of Statistical Learning Theory. New York: Springer.Google Scholar
Wan, X. 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of EMNLP 2008, Honolulu, HI.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Witten, I., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed.San Francisco: Morgan Kaufmann.Google Scholar
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.Google Scholar