Organizations: Independent Researcher · Centre for Applied Creative Technologies (CFACT+), Bournemouth University, UK · School of Computing, Newcastle University, Newcastle upon Tyne, UK
Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA from categorical polarity labels to continuous valence-arousal (VA) regression. This paper describes a system developed for Track A, Subtask 1 (Dimensional Aspect Sentiment Regression), aiming to predict real-valued VA scores in the [1, 9] range for each given aspect in a text. A fine-tuning approach based on XLM-RoBERTa-base is adopted, with dual regression heads with sigmoid-scaled outputs for valence and arousal prediction. Separate models are trained for each language-domain pair (English and Chinese across restaurant, laptop, and finance domains), and training and development sets are merged for final test predictions. In development experiments, the fine-tuning approach is compared against several large language models under a few-shot prompting setting, demonstrating that task-specific fine-tuning outperforms these LLM-based methods across all evaluation datasets.
Figures & tables
Figure 1: Model architecture.
Language
Domain
Train
Dev
Test
eng
Restaurant
2,284
200
1,000
eng
Laptop
4,076
200
1,000
zho
Restaurant
6,050
300
1,000
zho
Laptop
3,490
300
1,000
zho
Finance
1,000
200
842
Table 1: Dataset statistics (number of sentences).
Hyperparameter
Value
Pretrained model
XLM-RoBERTa-base
Max sequence length
256
Batch size
16
Learning rate
2e-5
Optimizer
AdamW
Warmup ratio
10%
Table 2: Hyperparameters for fine-tuning.
Method
eng_lap
eng_res
zho_lap
zho_res
zho_fin
Kimi-K2 Thinking
2.1893
2.1461
1.6440
1.8959
1.9652
Qwen-3 14B (QLoRA)
2.8089
2.6427
1.7706
2.0073
1.4707
XLM-RoBERTa
1.4562
1.4861
0.7510
0.9553
0.5391
Table 3: Test set RMSE VA compared with baselines (lower is better).
Figure 2: Validation RMSE VA across training epochs.
Method
eng_lap
eng_res
zho_lap
zho_res
zho_fin
GPT-5.2
1.9663
1.9203
1.5825
1.7279
1.7311
LLaMA-3-70B
1.6243
1.6748
1.9249
1.9589
2.2312
LLaMA-4-Mav.
1.6987
1.7955
1.6480
1.7718
1.7858
XLM-RoBERTa
1.1084
1.3402
0.8034
0.6727
0.5118
Table 4: RMSE VA comparison on the 20% held-out evaluation set (lower is better).
Figure 3: Visual comparison of RMSE VA across methods on the 20% held-out evaluation set.
Figure 4: Training data distribution in VA space. English datasets cluster in the high-valence, high-arousal region, while Chinese datasets are more centered.
Figure 5: Test-set RMSE VA heatmap across the VA space. Each cell shows the RMSE and sample count (n) for gold samples in that valence–arousal bin. Red indicates higher error; green indicates lower error.
Language
Domain
RMSE V
RMSE A
eng
Laptop
1.25
0.75
eng
Restaurant
1.11
0.98
zho
Laptop
0.57
0.48
zho
Restaurant
0.61
0.74
zho
Finance
0.44
0.31
Table 5: Test-set RMSE for valence (RMSE V ) and arousal (RMSE A ).
In this paper, we present AILS-NTUA system for Track-A of SemEval-2026 Task 3 on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which encompasses three complementary problems: Dimensional Aspect Sentiment Regression (DimASR), Dimensional Aspect Sentiment Triplet Extraction (DimASTE), and Dimensional Aspect Sentiment Quadruplet Prediction (DimASQP) within a multilingual and multi-domain framework. Our methodology combines fine-tuning of language-appropriate encoder backbones for continuous aspect-level sentiment prediction with language-specific instruction tuning of large language models using LoRA for structured triplet and quadruplet extraction. This unified yet task-adaptive design emphasizes parameter-efficient specialization across languages and domains, enabling reduced training and inference requirements while maintaining strong effectiveness. Empirical results demonstrate that the proposed models achieve competitive performance and consistently surpass the provided baselines across most evaluation settings.
Stavros Gazetas, Giorgos Filandrianos, Maria Lymperaiou +3
School of Electrical and Computer Engineering, AILS Laboratory National Technical University of Athens
This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. We investigate methods for moving beyond traditional categorical sentiment (e.g., positive or negative) to predict fine-grained, real-valued scores for sentiment "valence" (positivity) and "arousal" (intensity). We participate in two subtasks: predicting these scores for given aspects (Subtask 1) and extracting full sets of sentiment details, including aspects, categories, and opinions alongside their scores (Subtask 3). Our approach for the regression task involves a weighted ensemble of transformer-based encoder models. For the Russian language, we further enhance the input by using a large language model (LLM) to generate synthetic sentiment descriptions. For the extraction task, we fine-tune a decoder LLM to perform structured prediction, allowing the system to identify sentiment elements and estimate their numerical scores simultaneously.
We present the SemEval-2026 shared task on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which improves traditional ABSA by modeling sentiment along valence-arousal (VA) dimensions rather than using categorical polarity labels. To extend ABSA beyond consumer reviews to public-issue discourse (e.g., political, energy, and climate issues), we introduce an additional task, Dimensional Stance Analysis (DimStance), which treats stance targets as aspects and reformulates stance detection as regression in the VA space. The task consists of two tracks: Track A (DimABSA) and Track B (DimStance). Track A includes three subtasks: (1) dimensional aspect sentiment regression, (2) dimensional aspect sentiment triplet extraction, and (3) dimensional aspect sentiment quadruplet extraction, while Track B includes only the regression subtask for stance targets. We also introduce a continuous F1 (cF1) metric to jointly evaluate structured extraction and VA regression. The task attracted more than 400 participants, resulting in 112 final submissions and 42 system description papers. We report baseline results, discuss top-performing systems, and analyze key design choices to provide insights into dimensional sentiment analysis at the aspect and stance-target levels. All resources are available on our GitHub repository.
Liang-Chih Yu, Jonas Becker, Shamsuddeen Hassan Muhammad +14
Yuan Ze University · University of Göttingen · Imperial College London +10