Pawitsapak Akarajaradwong, Pirat Pothavorn, Chompakorn Chaksangchaichot, Panuthep Tasawong, Thitiwat Nopparatbundit, Keerakiat Pratai, Sarana Nutanong. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Mood, a long-lasting affective state detached from specific stimuli, plays an important role in behavior. Although sentiment analysis and emotion classification have garnered attention, research on mood classification remains in its early stages. This study adopts a two-dimensional structure of affect, comprising “pleasantness” and “activation,” to classify mood patterns. Emojis, graphic symbols representing emotions and concepts, are widely used in computer-mediated communication. Unlike previous studies that consider emojis as direct labels for emotion or sentiment, this work uses a pre-trained large language model which integrates both text and emojis to develop a mood classification model. Our contributions are three-fold. First, we annotate 10,000 Thai tweets with mood to train the models and release the dataset to the public. Second, we show that emojis contribute to determining mood to a lesser extent than text, far from mapping directly to mood. Third, through the application of the trained model, we observe the correlation of moods during the Thai political turmoil of 2019–2020 on Thai X (formerly known as Twitter) and find a significant correlation. These moods closely reflect the news events and reveal one side of Thai public opinion during the turmoil.