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Content Provider | IEEE Xplore Digital Library |
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Author | Inrak, P. Sinthupinyo, S. |
Copyright Year | 2010 |
Description | Author affiliation: Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand (Inrak, P.; Sinthupinyo, S.) |
Abstract | With a rapid growth of the internet communication, many types of text are produced. They can convey the meanings that can contribute to text categorization. Emotion classification also becomes more interesting, but emotion classification in Thai text is still not able to be correctly classified. Thus, this paper proposes a novel approach that takes advantage of bi-words occurrence to classify emotion hidden in a short sentence. In this paper, we classify Thai text into six basic universal emotions including anger, disgust, fear, happiness, sadness, and surprise based on latent semantic analysis approach. We compared the results between two models which construct features from the sentences and applied to three classification methods, i.e. Naïve Bayes, SVM, and Decision Tree. The first feature model uses only single word occurrence in the classification. The second model uses single word combined with bi-words occurrence in the classification. The results show that the second model can yield higher accuracy than the first model based on the Naïve Bayes classification method. |
File Size | 184663 |
File Format | |
ISBN | 9781424463473 |
e-ISBN | 9781424463497 |
DOI | 10.1109/ICCET.2010.5486137 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-04-16 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Dictionaries Statistical analysis Matrix decomposition Emotions in Text Affective Computing Support vector machines Text categorization Support vector machine classification Linear algebra Latent Semantic Analysis Internet Decision trees Singular value decomposition |
Content Type | Text |
Resource Type | Article |
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