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Author: search.filters.author.Lertsuksakda, RathawutSubject: search.filters.subject.Sentiment analysis

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    Hypothesis testing based on observation from Thai sentiment classification
    (2017-06-01)
    Netisopakul, Ponrudee
    ;
    Pasupa, Kitsuchart
    ;
    Lertsuksakda, Rathawut
    This work focuses on error analyzes from the Support Vector Machine (SVM) classification on Thai children stories at a sentence level. The construction of the Sentiment Term Tagging System (STTS) program allows the researchers to make observations and hypothesize around the areas where most anomalies occur. Three hypotheses, based on terms sentiment chosen for SVM predictions, are evidently proved to hold. In addition, a number of ways to improve the Thai sentiment classification research are suggested, including considerations to add negation into the process, add weighing scheme for different part-of-speech, disambiguate word senses, and update the Thai sentiment resource.
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    Sentiment analysis of Thai children stories
    (2016-09-01)
    Pasupa, Kitsuchart
    ;
    Netisopakul, Ponrudee
    ;
    Lertsuksakda, Rathawut
    Sentiment Text Tagging System (STTS) with Thai sentiment resource has been developed and used to tag emotions directly to words and sentences in Thai children stories. The Thai sentiment resource, developed from SenticNet2 resource, groups emotions into four independent but concomitant dimensions: pleasantness, attention, sensitivity and aptitude. The measure of each dimension is called a sentic value of that dimension. Thai sentiment resource stores each word’s sentic value and polarity value, a value calculated from the sentic value, in the form of floating point number. The resource was constructed from bi-directional translation of 14,244 English terms in SenticNet2 into 16,584 Thai terms. The main purpose of this study was to implement a sentiment analysis of Thai children stories system with support vector machine using a set of proposed discriminating features for classifying emotions. It was found that the system can achieve 75.67 % of accuracy.
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    Thai sentiment terms construction using the hourglass of emotions
    (2014-01-01)
    Lertsuksakda, Rathawut
    ;
    Netisopakul, Ponrudee
    ;
    Pasupa, Kitsuchart
    Most of the current sentiment analysis techniques classifies emotions into two classes which are positive and negative. Some works classify them as positive, negative and objective (neutral). In fact, there are many kinds of emotions in human mind. Recently, psychological viewpoints have influenced most of the works in sentiment analysis. This psychology perspective was adopted to classify human emotions into a wider range, and in a more accurate manner. This paper reviews the adopted computational representation of emotions the so-called Hourglass of Emotion. This paper also proposes a construction of Thai sentiment resource based on such representation for Thai sentiment term tagging. A preliminary sentiment text tagging result shows that the resource as an ontology can be successfully used to tag sentiment text in Thai children stories. © 2013 IEEE.