SalAngaBhava is a new, publicly released Sinhala e-commerce review dataset with 1,858 reviews manually annotated at the aspect-sentiment quadruple level for ABSA research.
DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA research relies on coarse-grained categorical labels (e.g., positive, negative), which limits its ability to capture nuanced affective states. To address this limitation, we adopt a dimensional approach that represents sentiment with continuous valence-arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels. To this end, we introduce DimABSA, the first multilingual, dimensional ABSA resource annotated with both traditional ABSA elements (aspect terms, aspect categories, and opinion terms) and newly introduced VA scores. This resource contains 76,958 aspect instances across 42,590 sentences, spanning six languages and four domains. We further introduce three subtasks that combine VA scores with different ABSA elements, providing a bridge from traditional ABSA to dimensional ABSA. Given that these subtasks involve both categorical and continuous outputs, we propose a new unified metric, continuous F1 (cF1), which incorporates VA prediction error into standard F1. We provide a comprehensive benchmark using both prompted and fine-tuned large language models across all subtasks. Our results show that DimABSA is a challenging benchmark and provides a foundation for advancing multilingual dimensional ABSA. We publicly released the DimABSA dataset, which was used for Track A of SemEval-2026 Task 3, attracting over 300 participants.
fields
cs.CL 4years
2026 4representative citing papers
Temporal simultaneity of annotations is the dominant predictor of inter-annotator agreement in a Setswana sentiment corpus, with kappa falling from 0.98 to 0.65 as time separation increases.
The paper introduces the DimABSA shared task for SemEval-2026 that reformulates aspect-based sentiment analysis and stance detection as valence-arousal regression problems with subtasks for regression, triplet, and quadruplet extraction plus a new continuous F1 metric.
Fine-tuning XLM-RoBERTa-base with separate models per language-domain pair outperforms few-shot LLMs for multilingual dimensional aspect sentiment regression.
citing papers explorer
-
SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
SalAngaBhava is a new, publicly released Sinhala e-commerce review dataset with 1,858 reviews manually annotated at the aspect-sentiment quadruple level for ABSA research.
-
Temporal Simultaneity Predicts Annotation Quality in Sentiment Corpora
Temporal simultaneity of annotations is the dominant predictor of inter-annotator agreement in a Setswana sentiment corpus, with kappa falling from 0.98 to 0.65 as time separation increases.
-
SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)
The paper introduces the DimABSA shared task for SemEval-2026 that reformulates aspect-based sentiment analysis and stance detection as valence-arousal regression problems with subtasks for regression, triplet, and quadruplet extraction plus a new continuous F1 metric.
-
NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression
Fine-tuning XLM-RoBERTa-base with separate models per language-domain pair outperforms few-shot LLMs for multilingual dimensional aspect sentiment regression.