Applying latent semantic analysis to classify emotions in Thai text

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.