Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks

2026-02-26

AI Summary

This study addresses the scarcity of aspect-based sentiment analysis (ABSA) resources for low-resource languages like Czech, particularly the lack of datasets annotated with opinion terms and effective cross-lingual transfer methods. To bridge this gap, the authors construct the first Czech ABSA benchmark dataset in the restaurant domain, featuring opinion-term annotations and supporting three levels of task complexity. They systematically evaluate a range of Transformer and large language models (LLMs) under monolingual, cross-lingual, and multilingual settings, and propose an LLM-driven translation–label alignment strategy to enhance cross-lingual transfer. Experimental results demonstrate that the proposed approach significantly outperforms baseline methods on Czech ABSA, while also revealing limitations of current models in capturing fine-grained opinion expressions, thereby establishing a new benchmark for sentiment analysis in low-resource languages.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper introduces a novel Czech dataset in the restaurant domain for aspect-based sentiment analysis (ABSA), enriched with annotations of opinion terms. The dataset supports three distinct ABSA tasks involving opinion terms, accommodating varying levels of complexity. Leveraging this dataset, we conduct extensive experiments using modern Transformer-based models, including large language models (LLMs), in monolingual, cross-lingual, and multilingual settings. To address cross-lingual challenges, we propose a translation and label alignment methodology leveraging LLMs, which yields consistent improvements. Our results highlight the strengths and limitations of state-of-the-art models, especially when handling the linguistic intricacies of low-resource languages like Czech. A detailed error analysis reveals key challenges, including the detection of subtle opinion terms and nuanced sentiment expressions. The dataset establishes a new benchmark for Czech ABSA, and our proposed translation-alignment approach offers a scalable solution for adapting ABSA resources to other low-resource languages.

Problem

• aspect-based sentiment analysis
• opinion terms
• low-resource languages
• Czech
• cross-lingual challenges

Innovation

• aspect-based sentiment analysis
• opinion term annotation
• low-resource language
• large language models
• translation-label alignment