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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2016 Vol.6(10): 801-808 ISSN: 2010-3689
DOI: 10.7763/IJIET.2016.V6.796

The Comparison of Algorithms for Thai-Sentence Classification

Thanyarat Nomponkrang and Charun Sanrach
Abstract—The development of an online discussion board in collaborative learning makes available of tracking behavior and collaboration among students. The classification of discussion sentences on the board is an essential mechanism used to describe students’ interaction patterns. This paper proposes feature extraction module that is used to extract the feature of Thai-sentence according to the sentence function. The module extracts two main features which are term binary (TB) of key phrase and term frequency (TF) of part-of-speech (POS).The TB term is used to indicate the presence of key phrase that cannot identified by POS and the TF term is used to calculate the term frequency-inverse document frequency (TF-IDF).Each feature is extracted from Thai-sentence to perform the data setswhichare1) TF of POS 2) TB and TF of POS 3) TF-IDF of POS and 4) TB and TF-IDF of POS. The performance of all data set is compared using 4 classification algorithms including: Decision Tree, Naïve Baye, K-nearest neighbor (k-NN) and Support vector machine (SVM).In this experiment shows the result in two dimensions which are the appropriate algorithm and the appropriate features to classify the Thai-sentence. The result is SVM algorithm is the optimal model on the dataset that have key phrase and TF-IDF term of POS.

Index Terms—Thai-sentence classification, feature extraction, classification performance, Decision Tree, Naïve Baye, k-NN, SVM.

The authors are with the Department of Computer Education, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand (e-mail: thanyaryt@fte.kmutnb.ac.th).

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Cite: Thanyarat Nomponkrang and Charun Sanrach, "The Comparison of Algorithms for Thai-Sentence Classification," International Journal of Information and Education Technology vol. 6, no. 10, pp. 801-808, 2016.

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