Sentiment Analysis on Thai Social Media Using Convolutional Neural Networks and Long Short-Term Memory

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Chalisa Jitboonyapinit
Paralee Maneerat
Nivet Chirawichitchai


The objective of this research purposes a sentiment analysis of Thai social media using deep learning techniques consisting of a convolutional neural network, long short-term memory, and a gated recurrent unit. This research was used to  test the algorithm with a wongnai product and service dataset and measured performance with accuracy. The experiment of this research found convolutional neural networks with long short-term memory outperform convolutional neural networks, long short-term memory, and gated recurrent units in classification accuracy, with an accuracy of 85.0%, followed by long short-term memory accuracy of 83.7%, convolutional neural network accuracy of 77.0% and finally gated recurrent unit an accuracy of 65.4% respectively. Therefore, the hybrid working model of a Convolutional Neural Network with long short-term memory is most suitable and effective for Thai sentiment analysis.

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Jitboonyapinit, C. ., Maneerat, P. ., & Chirawichitchai, N. (2023). Sentiment Analysis on Thai Social Media Using Convolutional Neural Networks and Long Short-Term Memory. INTERNATIONAL SCIENTIFIC JOURNAL OF ENGINEERING AND TECHNOLOGY (ISJET), 7(1), 74–80. Retrieved from
Research Article


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