基于文本挖掘的热点主题识别赋能电商运营研究

Practical Paths of Hot Topic Identification Empowering E-Commerce Operation Based on Text Mining

摘要: 社交媒体文本数据蕴藏着丰富的消费需求信号，如何从海量非结构化文本中精准识别具备商业价值的热点主题，成为电商行业发展中有待解决的难题。本文以文本挖掘为核心方法，搭建“数据采集–主题发现–热度评估–商业转化”的技术赋能框架，深入剖析BERTopic主题模型与消费意图识别机制在电商场景下的具体应用路径。研究发现，文本挖掘需与业务场景深度融合，通过“识别–匹配–响应–沉淀”的业务闭环，才能实现数据智能到商业智能的转化，为电商行业的热点运营工作提供可落地的理论指导与实践方案。

Abstract: Social media text data contain abundant signals of consumer demand. How to accurately identify hot topics with commercial value from massive unstructured text has become an urgent problem to be solved in the development of the e-commerce industry. Taking text mining as the core method, this study constructs a technology-enabled framework of “data collection - topic discovery - popularity evaluation - commercial transformation”, and deeply analyzes the specific application paths of the BERTopic model and consumer intent recognition mechanism in e-commerce scenarios. The findings show that text mining must be deeply integrated with business scenarios. Only through the business closed loop of “identification - matching - response - precipitation” can the transformation from data intelligence to business intelligence be realized, providing implementable theoretical guidance and practical solutions for hot-spot operation in the e-commerce industry.