Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 1-30
Abstract
This paper explores the application of various natural language processing (NLP) models for sentiment analysis on financial news articles sourced from Thai financial news websites, focusing on Thai-language data. The study evaluates machine learning and deep learning models, including Lo-gistic Regression, Bidirectional Long Short-Term Memory (Bi-LSTM), Con-volutional Neural Networks (CNN), WangChanBERTa, OpenAI’s GPT-3.5 and OpenThaiGPT. The models' performance is assessed using accuracy, precision, recall, and F1-score. The findings reveal that the Fine-tuned WangChanBERTa model achieved the highest accuracy of 0.84 on the test-ing set, demonstrating its superior ability in classifying sentiment in Thai financial news. BI-LSTM and CNN models also performed well, with test-ing accuracies of 0.781 and 0.791 In contrast, OpenAI’s GPT-3.5 and Open-ThaiGPT, which lacked fine-tuning and optimized prompts due to computa-tional constraints, exhibited practical limitations in resource-constrained settings.