Aspect-Based Sentiment Analysis in Thai Texts: A Comparative Study of Machine Learning and Neural Network Approaches - Matarat, Mr.Korakot (Indochina Applied Sciences, 2025)
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.