Large Language Models for Czech Aspect-Based Sentiment Analysis

2025-08-11

AI Summary

This study presents the first systematic evaluation of 19 multilingual and Czech-specific large language models (LLMs) on Czech Aspect-Based Sentiment Analysis (ABSA), covering zero-shot, few-shot, and full fine-tuning paradigms. Under a unified evaluation framework, we analyze the impact of model scale, architecture, multilinguality, and release date on aspect term extraction and sentiment polarity classification, complemented by fine-grained error analysis. Key findings include: (1) domain-specialized smaller models significantly outperform general-purpose LLMs in zero- and few-shot settings—especially for aspect term identification; (2) fine-tuned multilingual LLMs (e.g., mT5, BLOOMZ) achieve state-of-the-art performance on Czech ABSA; and (3) model release date and multilingual capability are not decisive performance factors—domain adaptation proves more critical. These results provide empirical guidance for model selection and optimization strategies in low-resource language ABSA.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

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

Abstract

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to identify sentiment toward specific aspects of an entity. While large language models (LLMs) have shown strong performance in various natural language processing (NLP) tasks, their capabilities for Czech ABSA remain largely unexplored. In this work, we conduct a comprehensive evaluation of 19 LLMs of varying sizes and architectures on Czech ABSA, comparing their performance in zero-shot, few-shot, and fine-tuning scenarios. Our results show that small domain-specific models fine-tuned for ABSA outperform general-purpose LLMs in zero-shot and few-shot settings, while fine-tuned LLMs achieve state-of-the-art results. We analyze how factors such as multilingualism, model size, and recency influence performance and present an error analysis highlighting key challenges, particularly in aspect term prediction. Our findings provide insights into the suitability of LLMs for Czech ABSA and offer guidance for future research in this area.

Problem

• Evaluating LLMs for Czech aspect-based sentiment analysis
• Comparing performance in zero-shot, few-shot, fine-tuning scenarios
• Analyzing factors like multilingualism and model size impact

Innovation

• Evaluating 19 LLMs for Czech ABSA
• Fine-tuning small domain-specific models
• Analyzing multilingualism, size, recency impacts