Pages 1–30

Authors

Nuttawut Thuayhanruksa and Pree Thiengburanathun

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.