This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production.

本研究探讨了利用大型语言模型生成合成数据集的方法，以测试产品吸引力工具包（PDT），填补了用户情感和产品体验评估中的空白。研究表明，虽然存在对积极情感的小偏见，但在数据不足的情况下，LLM生成的合成数据在可扩展性、成本节约和数据集生产灵活性方面具有显著优势。

利用大型语言模型合成产品吸引力数据集