\label{sec:Related Work}

Prior work on dimensional sentiment analysis has focused on building affective resources and developing regression models across multiple granularities.
On the resource side, sentiment lexicons provide word-level VA norms \cite{warriner-etal-2013-norms,mohammad-2018-obtaining}, while sentence-level corpora offer broader contextual coverage \cite{preotiuc-pietro-etal-2016-modelling,buechel-hahn-2017-emobank}. Chinese dimensional resources have also been developed to address the gap in non-English coverage \cite{yu-etal-2016-building,lee2022chinese}. On the modeling side, early approaches relied on LSTM-based architectures for VA prediction \cite{wu-etal-2017-thu,cheng2021valence,wang-etal-2016-dimensional}, while pre-trained Transformers subsequently became the dominant paradigm. For instance, by employing pre-trained BERT and MLP for V-A prediction,~\cite{xu-etal-2024-hitsz}, or incorporating contrastive learning approaches~\cite{tong-wei-2024-cciiplab}.
More recently, LLM-based methods have shown strong performance on dimensional sentiment tasks, either via in-context learning(ICL) or supervised fine-tuning(SFT) \cite{xu-etal-2024-hitsz}, and represent the prevailing approach in recent shared task competitions.