A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
This provides a new benchmark for retail-specific sentiment analysis, but it is incremental as it applies existing methods to new data.
The study tackled aspect-based sentiment analysis by introducing a manually annotated dataset of 10,814 multilingual retail reviews and evaluating GPT-4 and LLaMA-3, with both models achieving over 85% accuracy and GPT-4 outperforming LLaMA-3.
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.