Abstract
Emotion detection from text is still an appealing challenge. The approaches to this problem have been done firstly based on just emotional keywords, and then extended with utilizing also other generic terms. However, they still lack of some useful semantic features, such as a psychological characteristic that emotion is the result of a mental state sequence. Recent works focus on using rules to exploit those features, but have the coverage problem. In this paper, we propose a method using the high-order Hidden Markov Model whose states are automatically generated to model the process that a mental state sequence causes an emotion. Our experiments on the ISEAR dataset have shown a better result in comparison with the state-of-the-art methods.
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Ho, D.T., Cao, T.H. (2012). A High-Order Hidden Markov Model for Emotion Detection from Textual Data. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science(), vol 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_8
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DOI: https://doi.org/10.1007/978-3-642-32541-0_8
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