Hidden emotional trends on social media regarding the Thailand–China high-speed railway project: a deep learning approach with ChatGPT integration

Abstract

Leveraging sentiment analysis on social media reveals hidden emotional trends by providing deep insights into the public’s collective opinions and feelings because social media can reflect most opinions based on true feelings. However, there is no time-series analysis of Thai opinions on social media regarding government projects in Thailand. Therefore, the focus of this research is to survey and dynamically analyze sentiment on social media platforms, particularly YouTube, regarding the Thailand–China High-Speed Rail Project (Thai–China HSR Project). This is a major project involving an investment of over 500 billion baht, a collaborative effort between the Thai and Chinese governments aimed at developing transportation infrastructure. It leads the public concerns of Thai people about the potential economic and social impacts. The deep learning (DL) technique was used for this analysis by the Bidirectional Encoder Representations from Transformers (BERT) model, together with WangchanBERTa, a Thai language model specifically designed to analyze opinions from Thai comments received from YouTube from July 2015 to January 2024. The analysis reveals that citizens’ sentiments are primarily neutral, with more negative than positive sentiments with the passing years. Results also indicate that the data volume has increased since 2019 and peaked in 2022. Negative comments peaked in January 2022, and based on our analysis, such negative comments followed news covering the opening of the China–Laos Railway on December 1, 2021. Video contents were related to the Thai–China HSR Project, which provides comparative reviews and analysis of government operations. Additionally, we explore the possibility of integrating AI technology based on the ChatGPT 4 model to support data interpretation. Although the AI model can interpret data quickly and efficiently serve as a convenient method for users, the results lack the in-depth analysis that should be done by experts in the field. The proposed framework could be useful to government agencies and can be used to quickly and efficiently survey and track public opinion. The results can be used for policy planning, strategic decision-making, and improving communications to better inform citizens about projects and operations, leading to the country’s sustainable economic and social development.