构建多语言零售评论数据集，评估大模型在细粒度情感分析中的表现。
A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
- 构建10,814条多语言零售评论数据集，标注8类商品属性及情感倾向。
- GPT-4与LLaMA-3在该数据集上准确率均超85%，GPT-4整体表现更优。
- 为零售场景下的细粒度情感分析提供可复现的基准，适合模型评估研究者使用。
基于方面的情感分析通过将情感与具体方面关联，提供了比传统情感分析更深入的洞察。本研究引入一个包含10,814条多语言客户评论的手动标注数据集，覆盖实体零售门店，标注了8个方面类别及其情感极性。利用该数据集，评估了GPT-4和LLaMA-3在方面级情感分析中的性能，以建立新数据集的基线。结果表明，两个模型的准确率均超过85%，且在所有相关指标上GPT-4整体表现优于LLaMA-3。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。