Aspect-Based Sentiment Analysis in Thai Texts: A Comparative Study of Machine Learning and Neural Network Approaches

Mr.Korakot Matarat

doi:10.55674/ias.v14i3.262648

Authors

Mr.Korakot Matarat Faculty of Science and Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon, Thailand 0000-0001-9724-202X

DOI:

https://doi.org/10.55674/ias.v14i3.262648

Keywords:

Text classification, Machine learning, Thai language algorithms, Natural language processing, Bag of words with keras

Abstract

Efficiently classifying messages into document categories is a fundamental task in natural language processing, crucial for organizing and extracting insights from vast amounts of textual data. This paper explores the application of machine learning algorithms, particularly neural networks incorporating contextual and linguistic semantics, for the purpose of classifying texts. Unlike traditional subject-based classification, the focus here is on overall judgment, posing unique challenges. This study examines aspect-based sentiment analysis (ABSA), which depends on accurate text classification to identify entity aspects and their associated sentiments. Using Thai language review data and a list of 400K food words, the research compares several classifiers: Naive Bayes, Linear SVM, Logistic Regression, and Bag of Words (BoW) with Keras. Results show that BoW with Keras performs best, achieving 97 % accuracy after 10 training rounds, with steady improvements in accuracy and loss reduction across epochs. This paper not only presents models and methodologies applicable to Thai-language text classification but also introduces a proposed method for measuring Thai sentiment. While the study provides valuable insights, it acknowledges the necessity for considering diverse configurations and requirements, as alternative classifiers may yield comparable or superior results. The findings herein contribute to the ongoing discourse in the field and offer a foundation for further exploration and refinement of classification techniques in Thai language text processing.

References

A. Yusuf, K. Allix, L. Veiber, C. Lothritz, T.F. Bissyandé, J. Klein, A. Goujon, A comparison of pre-trained language models for multi-class text classification in the financial domain, WWW '21: Companion Proceedings of the Web Conference 2021, Machinery, New York, NY, United States, 19 – 23 April 2021, 260 – 268.

A.M.F. Al-Sbou, A survey of Arabic text classification models, IJ-ICT. 8(1) (2019) 25–28.

M. Bounabi, E.M. Karim, S. Khalid, Text classification using fuzzy TF-IDF and machine learning models, BDIoT '19: Proceedings of the 4th International Conference on Big Data and Internet of Things, Machinery, New York, NY, United States, 23 – 24 October 2019, 1 – 6.

R.B. Bhavana, P. Jeyanthi, A multilabel classifier for text classification and enhanced BERT system, Rev. Intell. Artif. 35(2) (2021) 167–176.

T.C. Bahzad, B.S. Amira, A comprehensive survey of deep learning models based on Keras framework, J. Soft Comput. Data Min. 2(2) (2021) 49 – 62.

J. Deng, L. Chen, Z. Wang, Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification, Comput. Speech Lang. 68 (2021) 101182.

E.C. Denis, P. Irene, Performance comparison of TF-IDF and Word2Vec models for emotion text classification, Bull. Electr. Eng. Inform. 10(5) (2021) 2780 – 2788.

D. Robert, S. Dmitrij, Text classification using different feature extraction approaches, 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), IEEE, Vilnius, Lithuania, 5 – 25 April 2019, 1 – 4.

F. Fanny, Y. Mulyana, T. Fidelson, A comparison of text classification methods k-NN, Naïve Bayes, and support vector machine for news classification, JPIT. 3(2) (2018) 157– 160.

J. Gong, Z. Teng, Q. Teng, H. Zhang, L. Du, S. Chen, M.Z.A. Bhuiyan, J. Li, M. Liu, H. Ma, Hierarchical graph transformer-based deep learning model for large-scale multi-label text classification, IEEE Access. 8 (2020) 30885 – 30896.

L. Huang, D. Ma, S. Li, X. Zhang, H. Wang, Text level graph neural network for text classification, arXiv preprint arXiv:1910.02356, 2019.

B.-M. Hsu, Comparison of supervised classification models on textual data, Mathematics. 8 (2020) 851.

K. Jamal, R. Kurniawan, A. S. Batubara, M.Z.A. Nazri, F. Lestari, P. Papilo, Text classification on Islamic jurisprudence using machine learning techniques, J. Phys. Conf. Ser. 1566(1) (2020) 12066.

M. Jiang, Y. Liang, X. Feng, X. Fan, Z. Pei, Y. Xue, R. Guan, Text classification based on deep belief network and softmax regression, Neural Comput. Appl. 29(1) (2018) 61–70.

R.S. Kathuria, S. Gautam, A. Singh, S. Khatri, N. Yadav, Real-time sentiment analysis on Twitter data using deep learning (Keras), 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), IEEE, Noida, India, 18 – 19 October 2019, 69–73.

K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, D. Brown, Text classification algorithms: A survey, Information. 10(4) (2019) 150.

J. Kim, S. Jang, E. Park, S. Choi, Text classification using capsules, Neurocomputing. 376 (2020) 214 – 221.

Y. Li, X. Wang, P. Xu, Chinese text classification model based on deep learning, Future Internet. 10(11) (2018) 113.

P. Jagadeesh, B. Jasmine, T.K.R. Tarun, M.R.C. Manish, Sentiment analysis of product reviews using deep learning, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Bangalore, India, 19 – 22 September 2018, 2408 – 2410.

I.M. Rabbimov, S.S. Kobilov, Multi-class text classification of Uzbek news articles using machine learning, J. Phys. Conf. Ser. 1546 (2020) 012097.

A. Rusli, A. Suryadibrata, S. Nusantara, J. Young, A comparison of traditional machine learning approaches for supervised feedback classification in Bahasa Indonesia, IJNMT. 7(1) (2020) 28 – 32.

X. She, Z. Di, Text classification based on hybrid CNN–LSTM hybrid model, 2018 11th International Symposium on Computational Intelligence and Design (ISCID), IEEE, Hangzhou, China, 08 – 09 December 2018, 185 – 189.

P.A. Telnoni, R. Budiawan, M. Qana’a, Comparison of machine learning classification method on text-based case in Twitter, 2019 International Conference on ICT for Smart Society (ICISS), IEEE, Bandung, Indonesia, 19 – 20 November 2019, 1 – 5.

K. Thirumoorthy, K. Muneeswaran, Feature selection for text classification using machine learning approaches, Natl. Acad. Sci. Lett. 45(1) (2022) 51 – 56.

G. Wang, S.Y. Shin, An improved text classification method for sentiment classification, J. Inf. Commun. Converg. Eng. 17(1) (2019) 41–48.

Y. Wu, J. Li, V. Chen, J. Chang, Z Ding, Z. Wang, Text classification using triplet capsule networks, in: International Joint Conference on Neural Networks (2020), IEEE, Glasgow, UK, 19 – 24 July 2020, 1–7.

X. Lu, Efficient English text classification using selected machine learning techniques, Alex. Eng. J. 60(3) (2021) 3401 – 3409.

X. Liu, H. Ni, Chinese text classification based on hybrid model of CNN and LSTM, DSIT 2020: 2020 3rd International Conference on Data Science and Information Technology, Machinery, New York, NY, United States, 26 August 2020, 129 – 134.

Y. Yan, W. Li, G. Chen, W. Li, An improved text classification method based on convolutional neural networks, CCRIS '20: Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System, Machinery, New York, NY, United States, 27 – 29 October 2020, 185 – 190.

P.K. Yechuri, S. Ramadass, Classification of image and text data using deep learning-based LSTM model, Trait. Signal. 38(6) (2021) 1809 – 1817.

W. Zhao, G.Y. Wang, B. Peng, Knowledge text classification based on virtual category tree, Rev. Intell. Artif. 33(1) (2019) 15 – 19.

C. Liu, G. Zhang, Z. Li, News text classification based on improved Bi-LSTM-CNN, 2018 9th International Conference on Information Technology in Medicine and Education (ITME), IEEE, Hangzhou, China,19 – 21 October 2018, 890 – 893.

P. Pasupa, T. Seneewong-Na-Ayudhaya, Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features, Sustain. Cities Soc. 50 (2019) 101615.

P. Sroison, J.H. Chan, Resume parser with natural language processing, TechRxiv. 29 December 2021.

E. Charoenphakdee, Wongnai data services, https://github.com/wongnai/wongnai-corpus/tree/master/review, Accessed 1 November 2021.

N. Khamphakdee, P. Seresangtakul, An efficient deep learning for Thai sentiment analysis, Data. 8(5) (2023) 90.

L. Ashbaugh, Y. Zhang, A comparative study of sentiment analysis on customer reviews using machine learning and deep learning, Computers. 13(12) (2024).