Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.17994 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918130203230208 |
|---|---|
| author | Silcenco, Oleg Machad, Marcos R. Ugulino, Wallace C. Braun, Daniel |
| author_facet | Silcenco, Oleg Machad, Marcos R. Ugulino, Wallace C. Braun, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models Silcenco, Oleg Machad, Marcos R. Ugulino, Wallace C. Braun, Daniel Computation and Language 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. |
| title | A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.17994 |