Abstract
In this paper, we adopt a supervised machine learning approach to recognize six basic emotions (anger, disgust, fear, happiness, sadness and surprise) using a heterogeneous emotion-annotated dataset which combines news headlines, fairy tales and blogs. For this purpose, different features sets, such as bags of words, and N-grams, were used. The Support Vector Machines classifier (SVM) performed significantly better than other classifiers, and it generalized well on unseen examples.
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References
Alm, C.O.: Affect in Text and Speech. PhD Dissertation. University of Illinois at Urbana- Champaign (2008)
Aman, S., Szpakowicz, S.: Identifying expressions of emotion in text. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 196–205. Springer, Heidelberg (2007)
Aman, S.: Identifying Expressions of Emotion in Text, Master’s thesis, University of Ottawa, Ottawa, Canada (2007)
Arora, S., Mayfield, E., Penstein-Ros, C., Nyberg, E.: Sentiment Classification using Automatically Extracted Subgraph Features. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (2010)
Ekman, P., Friesen, W.V.: Facial action coding system: Investigator’s guide. Consulting Psychologists Press, Palo Alto (1978)
Izard, C.E.: The Face of Emotion. Appleton-Century-Crofts, New York (1971)
Melville, P., Gryc, W., Lawrence, R.: Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification. In: Proc. of KDD, pp. 1275–1284 (2009)
Neviarouskaya, A., Prendinger, H., Ishizuka, M.: @AM: Textual Attitude Analysis Model. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Los Angeles, USA (2010)
Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Compositionality Principle in Recognition of Fine-Grained Emotions from Text. In: Proceedings of the International Conference on Weblogs and Social Media. AAAI, San Jose (2009)
Paulmann, S., Pell, M.D.: Dynamic emotion processing in Parkinson’s disease as a function of channel availability. Journal of Clinical and Experimental Neuropsychology 32(8), 822–835 (2010)
Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)
Strapparava, C., Mihalcea, R.: Semeval- 2007 task 14: Affective text. In: Proceedings of the 4th International Workshop on the SemEval 2007, Prague (2007)
Strapparava, C., Valitutti, A., Stock, O.: The affective weight of lexicon. In: Proceedings of the Fifth International Conference on Language Resources and Evaluation, Genoa, Italy (2006)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Chaffar, S., Inkpen, D. (2011). Using a Heterogeneous Dataset for Emotion Analysis in Text. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_8
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DOI: https://doi.org/10.1007/978-3-642-21043-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21042-6
Online ISBN: 978-3-642-21043-3
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