Application of Short Text Classification Model Based on GPT-3.5 in E-Commerce

作者：Yapeng Peng, Guangming Wang, Jiaqi Guo, Zhaoqi Wang · 发表于：Journal of Organizational and End User Computing · 年份：2024 · DOI：10.4018/joeuc.356500 · 被引用次数：3 · 研究领域：Sentiment Analysis and Opinion Mining

In the realm of e-commerce, the crucial role of short text classification in enhancing user experience and platform operational efficiency has led to the development of effective. approaches encompassing feature-based algorithms and early deep learning models. However, challenges remain when dealing with complex semantics and vast amounts of unstructured data. To bridge this gap, This paper proposes a novel model of short text classification based on GPT-3.5, which significantly improves classification accuracy by combining conceptualization and character-level information. Our model's superior performence across all datasets was validated through experimentsal trials on five widely used datasets (TREC, AG News, Bing, and Movie Review).