Liu Yujia, Zhang Dong, Wan Qian, Lin Zhongzhen
School of Art and Design, Guangdong University of Technology, Guangzhou 510090, China.
Shenzhen Lingdong Software Development Co., Ltd., Shenzhen 518064, China.
Sensors (Basel). 2025 Mar 11;25(6):1731. doi: 10.3390/s25061731.
As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas-performance, comfort and experience, price sensitivity, and safety-while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries.
随着汽车行业的快速发展，用户需求也在不断演变。在大数据、人工智能和自然语言处理技术进步的推动下，从社交媒体上的用户生成内容（UGC）中挖掘用户需求已成为了解这些动态需求的有效途径。虽然现有技术在主题识别和情感分析方面取得了进展，但单一方法往往存在局限性。本研究提出了一种基于BERTopic和RoBERTa的用户需求挖掘新方法，结合主题建模和情感分析的优势，对用户需求进行更全面的分析。为了验证这种方法，收集了来自中国四大媒体平台的UGC数据。使用BERTopic进行主题提取，RoBERTa进行情感分析，便于对用户情感和识别出的主题进行关联分析。研究结果将用户需求分为四个主要领域——性能、舒适与体验、价格敏感度和安全——同时也反映出传感器、动力总成性能等先进功能及其他技术的相关性日益增加。该方法通过将情感分析与主题建模相结合，增强了用户需求识别能力，为汽车制造商在产品优化和营销策略方面提供了可操作的见解，并提出了一种可扩展的方法，适用于各个行业。
