Aspect-based sentiment analysis enhances sentiment detection by associating it with specific aspects, offering deeper insights than traditional sentiment analysis. This study introduces a manually annotated dataset of 10,814 multilingual customer reviews covering brick-and-mortar retail stores, labeled with eight aspect categories and their sentiment. Using this dataset, the performance of GPT-4 and LLaMA-3 in aspect based sentiment analysis is evaluated to establish a baseline for the newly introduced data. The results show both models achieving over 85% accuracy, while GPT-4 outperforms LLaMA-3 overall with regard to all relevant metrics.
@article{arxiv.2508.17994,
title = {A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models},
author = {Oleg Silcenco and Marcos R. Machad and Wallace C. Ugulino and Daniel Braun},
journal= {arXiv preprint arXiv:2508.17994},
year = {2025}
}