Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks
arXiv:2602.22730v1 Announce Type: new 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 b
arXiv:2602.22730v1 Announce Type: new 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.
Executive Summary
This study introduces a novel Czech dataset for aspect-based sentiment analysis (ABSA) in the restaurant domain, enriched with opinion term annotations. The dataset supports three complex ABSA tasks, and extensive experiments are conducted using modern Transformer-based models, including large language models (LLMs), in monolingual, cross-lingual, and multilingual settings. The proposed translation and label alignment methodology leveraging LLMs yields consistent improvements and addresses cross-lingual challenges. The results highlight the strengths and limitations of state-of-the-art models, especially when handling low-resource languages. The study establishes a new benchmark for Czech ABSA and offers a scalable solution for adapting ABSA resources to other low-resource languages. The findings have significant implications for natural language processing (NLP) and artificial intelligence (AI) applications, particularly in the tourism and hospitality sectors.
Key Points
- ▸ Introduction of a novel Czech dataset for ABSA in the restaurant domain
- ▸ Enrichment of the dataset with opinion term annotations
- ▸ Development of a translation and label alignment methodology leveraging LLMs
Merits
Contribution to the field of NLP
The study provides a valuable contribution to the field of NLP by introducing a novel dataset and methodology for ABSA in a low-resource language like Czech.
Scalable solution for adapting ABSA resources
The proposed translation and label alignment methodology offers a scalable solution for adapting ABSA resources to other low-resource languages.
Establishment of a new benchmark for Czech ABSA
The study establishes a new benchmark for Czech ABSA, which can be used to evaluate the performance of ABSA models and methodologies.
Demerits
Limited generalizability of results
The study's findings may not be generalizable to other languages or domains, and more research is needed to confirm the results.
Dependence on LLMs for translation and label alignment
The proposed methodology relies on LLMs, which may not be available or accessible to all researchers or practitioners.
Expert Commentary
The study provides a significant contribution to the field of NLP, particularly in the area of ABSA. The introduction of a novel Czech dataset and the development of a translation and label alignment methodology leveraging LLMs offer a scalable solution for adapting ABSA resources to other low-resource languages. However, the study's findings may not be generalizable to other languages or domains, and more research is needed to confirm the results. The use of LLMs for translation and label alignment also raises concerns about their dependence on these models. Overall, the study has significant implications for the development of ABSA models and methodologies, particularly in the tourism and hospitality sectors.
Recommendations
- ✓ Further research is needed to confirm the study's findings and generalize them to other languages and domains
- ✓ Investigation of the potential policy implications of the use of LLMs in NLP applications
