Sentiment Analysis of Thai Online Product Reviews using Genetic Algorithms with Support Vector Machine

Article Sidebar

PDF
Published: Nov 16, 2020
DOI: https://doi.org/10.14456/past.2020.8
Keywords:
Genetic Algorithms Sentiment Analysis Support Vector Machine

Main Article Content

Rawisuda Tesmuang
Faculty of Information Technology, Sripatum University
Nivet Chirawichitchai
Faculty of Information Technology, Sripatum University

Abstract

This research purposes sentiment analysis of Thai online product reviews for hotel room services, hotels, and resorts with a collection of 4,000 sample data sets. A Modeling with Genetic Algorithms with 4 machine learning methods is created. It consists of Support Vector Machine, Decision Tree, Naïve-Bayes, and K-Nearest Neighbor to compare the effectiveness of each method in analyzing sentiment analysis of the online products. The experiment found that the use of Genetic Algorithms with support vector machines provide better classification accuracy than using vector support machines with an accuracy of 88.64% and the proposed model can effectively reduce the dimensions of the data.

Article Details

How to Cite
1.
Tesmuang R, Chirawichitchai N. Sentiment Analysis of Thai Online Product Reviews using Genetic Algorithms with Support Vector Machine. Prog Appl Sci Tech. [Internet]. 2020 Nov. 16 [cited 2024 Nov. 15];10(2):7-13. Available from: https://ph02.tci-thaijo.org/index.php/past/article/view/241933
Issue
Vol. 10 No. 2 (2020): July - December 2020
Section
Information and Communications Technology
Crossref
Scopus
Google Scholar
Europe PMC

References

Katrekar A, AVP BD. An introduction to sentiment analysis. GlobalLogic Inc. 2005.

Pang B, Lee L. Opinion mining and sentiment analysis Foundations and Trends in Information Retrieval Vol. 2.

Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070. 2002 May 28.

Janpla S, Wanapiron P. System Framework for an Intelligent Question Bank and Examination System. Int. J. Mach. Learn. Comput. 2018 Oct;8(5).

Mitchell M. An introduction to genetic algorithms. MIT press; 1998.

Sıvanandam SN, Deepa SN. Introduction to Genetic Algorithm. Springer Science & Business Media Publisher; 2008.

Khan K, Baharudin BB, Khan A. Mining opinion from text documents: A survey. In2009 3rd IEEE International Conference on Digital Ecosystems and Technologies 2009 . Jun 1 pp. 217-222.

Salzberg SL. C4. 5: Programs for machine learning by j. ross quinlan. morgan kaufmann publishers, inc., 1993.

Chirawichitchai N. Automatic Thai Document Classification Model. J. Ind. Technol. 2013 Jan;9(1).

Chirawichitchai N, Sanguansat P, Meesad P. Automatic Thai Document Categorization with Support Vector Machines. InProceedings of the 6th National Conference on Computing and Information Technology 2010.

Joachims T. Text categorization with support vector machines: Learning with many relevant features. InEuropean conference on machine learning 1998 Apr 21. pp. 137-142.

Kongthon A, Angkawattanawit N, Sangkeettrakarn C, Palingoon P, Haruechaiyasak C. Using an opinion mining approach to exploit web content in order to improve customer relationship management. InPICMET 2010 Technology Management for Global Economic Growth 2010 Jul 18. pp.1-6.

Joachims T. Text categorization with support vector machines: Learning with many relevant features. InEuropean conference on machine learning 1998 Apr 21. pp.137-142.

Chirawichitchai N. Developing term weighting scheme based on term occurrence ratio for sentiment analysis. InInformation Science and Applications 2015 pp. 737-744.

Chirawichitchai N. Sentiment classification by a hybrid method of greedy search and multinomial naïve bayes algorithm. In2013 Eleventh international conference on ICT and knowledge engineering 2013 Nov 20. pp.1-4.