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An Efficient Deep Learning for Thai Sentiment Analysis

Author

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  • Nattawat Khamphakdee

    (Natural Language and Speech Processing Research Group, Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Pusadee Seresangtakul

    (Natural Language and Speech Processing Research Group, Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

The number of reviews from customers on travel websites and platforms is quickly increasing. They provide people with the ability to write reviews about their experience with respect to service quality, location, room, and cleanliness, thereby helping others before booking hotels. Many people fail to consider hotel bookings because the numerous reviews take a long time to read, and many are in a non-native language. Thus, hotel businesses need an efficient process to analyze and categorize the polarity of reviews as positive, negative, or neutral. In particular, low-resource languages such as Thai have greater limitations in terms of resources to classify sentiment polarity. In this paper, a sentiment analysis method is proposed for Thai sentiment classification in the hotel domain. Firstly, the Word2Vec technique (the continuous bag-of-words (CBOW) and skip-gram approaches) was applied to create word embeddings of different vector dimensions. Secondly, each word embedding model was combined with deep learning (DL) models to observe the impact of each word vector dimension result. We compared the performance of nine DL models (CNN, LSTM, Bi-LSTM, GRU, Bi-GRU, CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU) with different numbers of layers to evaluate their performance in polarity classification. The dataset was classified using the FastText and BERT pre-trained models to carry out the sentiment polarity classification. Finally, our experimental results show that the WangchanBERTa model slightly improved the accuracy, producing a value of 0.9225, and the skip-gram and CNN model combination outperformed other DL models, reaching an accuracy of 0.9170. From the experiments, we found that the word vector dimensions, hyperparameter values, and the number of layers of the DL models affected the performance of sentiment classification. Our research provides guidance for setting suitable hyperparameter values to improve the accuracy of sentiment classification for the Thai language in the hotel domain.

Suggested Citation

  • Nattawat Khamphakdee & Pusadee Seresangtakul, 2023. "An Efficient Deep Learning for Thai Sentiment Analysis," Data, MDPI, vol. 8(5), pages 1-22, May.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:5:p:90-:d:1146474
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    References listed on IDEAS

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    1. Ebaa Fayyoumi & Sahar Idwan, 2021. "Semantic Partitioning and Machine Learning in Sentiment Analysis," Data, MDPI, vol. 6(6), pages 1-17, June.
    2. Yahya Tashtoush & Balqis Alrababah & Omar Darwish & Majdi Maabreh & Nasser Alsaedi, 2022. "A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms," Data, MDPI, vol. 7(5), pages 1-17, May.
    3. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
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