A Hierarchical Emotion Classification Technique for Thai Reviews
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2018.12.3.6Keywords:
hierarchical emotion classification, speech synthesis, opinion classification, text processing, Thai languageAbstract
Emotion classification is an interesting problem in affective computing that can be applied in various tasks, such as speech synthesis, image processing and text processing. With the increasing amount of textual data on the Internet, especially reviews of customers that express opinions and emotions about products. These reviews are important feedback for companies. Emotion classification aims to identify an emotion label for each review. This research investigated three approaches for emotion classification of opinions in the Thai language, written in unstructured format, free form or informal style. Different sets of features were studied in detail and analyzed. The experimental results showed that a hierarchical approach, where the subjectivity of the review is determined first, then the polarity of opinion is identified and finally the emotional label is calculated, yielded the highest performance, with precision, recall and F-measure at 0.691, 0.743 and 0.709, respectively.
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References
Cowie, R., Douglas-Cowie E., Savvidou, S., McMahon E., Sawey, M. & Schroder M., Emotion Recognition in Human-computer Interaction, IEEE Signal Processing Magazine, 18(1), pp. 32-80, Jan. 2001.
Donato, G., Bartlett, M.S., Hager, J.C., Ekman, P. & Sejnowski, T.J., Classifying Facial Actions, IEEE Trans Pattern Anal Mach Intell, 21(10), pp. 974-989, Oct. 1999.
Cohen, I., Sebe, N., Garg, A., Chen, L.S. & Huang, T.S., Facial Expression Recognition from Video Sequences: Temporal and Static Modeling, Computer Vision and Image Understanding, 91(1-2), pp. 160-187, Jul. 2003.
Mehta, D., Siddiqui, M.F.H. & Javaid, A.Y., Facial Emotion Recognition: A Survey and Real-world User Experiences in Mixed Reality, Sensors, 18(2), pp. 416, Feb. 2018.
Strapparava, C. & Mihalcea, R., Learning to Identify Emotions in Text, in Proceedings of the 2008 ACM Symposium on Applied Computing, New York, NY, USA, pp. 1556-1560, 2008.
Chirawichitchai, N., Emotion Classification of Thai Text Based Using Term Weighting and Machine Learning Techniques, presented at the 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 91-96, 2014.
Inrak, P. & Sinthupinyo, S., Applying Latent Semantic Analysis to Classify Emotions in Thai Text, presented at the 2010 2nd International Conference on Computer Engineering and Technology, 6, pp. V6-450- 454, 2010.
Burget, R. & Karasek, J., Recognition of Emotions in Czech Newspaper Headlines, 20(1), pp. 39-47, 2011.
Sriphaew, K., Takamura, H. & Okumura, M., Sentiment Analysis for Thai Natural Language Processing, in Proceedings of the 2nd Thailand-Japan International Academic Conference TJIA, pp. 123-124, 2009.
Haruechaiyasak, C., Kongthon, A., Palingoon, P. & Trakultaweekoon, K., S-Sense: A Sentiment Analysis Framework for Social Media Sensing, in Proceedings of the IJCNLP 2013 Workshop on Natural Language Processing for Social Media (SocialNLP), Nagoya, Japan, pp. 6-13, 2013.
Chumwatana, T., Using Sentiment Analysis Technique for Analyzing Thai Customer Satisfaction from Social Media, presented at the Proceedings of the 5th International Conference on Computing and Informatics, Turkey, pp. 659-664, 2015.
Lui, B., Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, 2012.
Lee, D,. Jeong, O-R. & Lee, S., Opinion Mining of Customer Feedback Data on the Web, p. 230, 2008.
P. Ekman, An Argument for Basic Emotions, Cognition and Emotion, 6 (3-4), pp. 169-200, May 1992.
Breazeal, C., Emotion and Sociable Humanoid Robots, International Journal of Human-Computer Studies, 59(1-2), pp. 119-155, Jul. 2003.
Sudprasert, S. & Kawtrakul, A., Thai Word Segmentation Based on Global and Local Unsupervised Learning, in Proceedings of the 7th National Computer Science and Engineering Conference (NCSEC'2003), pp. 1-8, 2003.
Kok, D. de, Jitar HMM Part of Speech Tagger, https://github.com/danieldk/jitar, 2018. (9 July 2018)
Asanee, K., Supapas, K., Thitima, J. & Chanvit, J., A Lexibase Model for Writing Production Assistant System, in Proceeding of the Second Symposium on Natural Language Processing, pp. 226-236, 1995.
Liu, B., Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd ed. Berlin Heidelberg: Springer-Verlag, 2011.
Phatthiyaphaibun, W., lexicon-thai/sentiment at master PyThaiNLP/lexicon-thai GitHub, 2016. [Online]. Available: https://github.com/PyThaiNLP/lexicon-thai/tree/master/sentiment. (9 July 2018).
Salzberg, S.L., C4.5: Programs for Machine Learning by J. Ross Quinlan, Morgan Kaufmann Publishers, Inc., 1993,' Mach Learn, 16(3), pp. 235-240, Sep. 1994.
McCallum, A. & Nigam, K., A Comparison of Event Models for Naive Bayes Text Classification, in AAAI-98 Workshop on Learning for Text Categorization, pp. 41-48, 1998.
Yekkehkhany, B., Safari, A., Homayouni, S. & Hasanlou, M., A Comparison Study of Different Kernel Functions for SVM-based Classification of Multi-temporal Polarimetry SAR Data, in ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W3, pp. 281-285, 2014.
