{"ID":2878563,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17994","arxiv_id":"2508.17994","title":"A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models","abstract":"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.","short_abstract":"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 aspec...","url_abs":"https://arxiv.org/abs/2508.17994","url_pdf":"https://arxiv.org/pdf/2508.17994v1","authors":"[\"Oleg Silcenco\",\"Marcos R. Machad\",\"Wallace C. Ugulino\",\"Daniel Braun\"]","published":"2025-08-25T13:02:05Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}