🤖 AI Summary
This work addresses the limitations of traditional sentiment analysis, which relies on discrete categorical labels and struggles to model fine-grained, continuous emotional dimensions—such as valence and arousal—and their manifestation at the aspect level. To overcome this, the authors propose a novel approach for dimensional aspect-based sentiment analysis. It first employs a Transformer encoder to jointly predict aspect-specific valence and arousal scores. Second, it innovatively leverages large language models (LLMs) to generate synthetic Russian-language affective descriptions, thereby enhancing multilingual dimensional sentiment regression, particularly for low-resource languages. Finally, the method fine-tunes a decoder-based LLM to enable end-to-end structured extraction of aspects, opinions, categories, and their corresponding sentiment scores. Evaluated on SemEval-2026 Task 3, the approach significantly improves both sentiment score prediction and structured extraction performance for Russian and other low-resource languages.
📝 Abstract
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.