\label{sec:introduction}

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment expressed toward specific aspects in text \cite{pontiki-etal-2014-semeval,zhang-etal-2022-survey}.
Most existing ABSA studies formulate the task as coarse-grained classification (e.g., \textit{positive}, \textit{negative}, \textit{neutral}), which cannot capture fine-grained affective distinctions such as those between \textit{good} and \textit{excellent}.
Drawing on the theory from affective science \cite{russell1980circumplex}, sentiment can be represented along fine-grained, real-valued dimensions of \textbf{valence} (negative–positive) and \textbf{arousal} (sluggish–excited).

The DimABSA shared task \cite{yu-etal-2026-semeval} introduces a multilingual and multi-domain benchmark 
that integrates dimensional sentiment analysis into the traditional ABSA framework.
We focus on Track A Subtask~1, Dimensional Aspect Sentiment Regression (DimASR), which requires predicting continuous valence--arousal (VA) scores (1--9 scale) for a given aspect within a text (Figure~\ref{fig:task}).

\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{figure/task.png}
    \caption{Illustration of Subtask~1 (DimASR).}
    \label{fig:task}
\end{figure}


While large language models (LLMs) have become a dominant strategy for sentiment tasks, such approaches often involve high computational cost and limited reproducibility. 
In contrast, we investigate how competitive a lightweight system can be without using LLMs.

In this paper, we present a lightweight and resource-efficient system built entirely on multilingual pre-trained encoders without leveraging LLMs, external corpora, or data augmentation techniques. 
We adopt a joint multilingual and multi-domain training strategy to facilitate cross-lingual transfer and alleviate data scarcity. 
We further introduce a sigmoid-based bounded output transformation to improve training stability and ensure predictions within the valid range. 
Finally, we apply an adaptive ensemble strategy via exhaustive search to select the optimal subset from the candidate model pool for each specific language--domain pair, significantly reducing prediction variance.

Our system achieves strong performance across all 10 language--domain datasets, ranking 1st on \textit{zho-res}, 2nd on \textit{zho-lap}, and 3rd on \textit{jpn-hot}, with all remaining datasets placed within the top half of participating teams. 
These results suggest that, in dimensional sentiment regression, less is more: a carefully designed lightweight encoder-based system without LLMs can remain highly competitive against approaches that demand far greater computational resources.