Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish

TL;DR

This paper introduces two new Polish datasets for Aspect-Sentiment Triplet Extraction, enabling research in a previously underrepresented Slavic language and evaluating existing methods on these datasets.

Contribution

The paper provides the first Polish ASTE datasets and benchmarks their use with large language models, filling a gap in sentiment analysis resources for Slavic languages.

Findings

Datasets are publicly available under a permissive license.

Experiments show the performance of current ASTE techniques on Polish.

The datasets reveal the complexity of ASTE in Polish language context.

Abstract

Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. It concerns the construction of triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarity. Despite the growing popularity of the task and the many machine learning methods being proposed to address it, the number of datasets for ASTE is very limited. In particular, no dataset is available for any of the Slavic languages. In this paper, we present two new datasets for ASTE containing customer opinions about hotels and purchased products expressed in Polish. We also perform experiments with two ASTE techniques combined with two large language models for Polish to investigate their performance and the difficulty of the assembled datasets. The new datasets are available under a permissive licence and have the same file format as the English datasets, facilitating their use in future research.