Prompt-Based Approach for Czech Sentiment Analysis

Jakub Šmíd; Pavel Přibáň

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arXiv · 2508.08651

Prompt-Based Approach for Czech Sentiment Analysis

Jakub Šmíd·Pavel Přibáň

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Abstract

This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our prompt-based approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario.

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BibTeX RIS

Jakub Šmíd, Pavel Přibáň. 2025-08-12. Prompt-Based Approach for Czech Sentiment Analysis. https://arxiv.org/abs/2508.08651

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