Abstract
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).
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