电商中用户弹幕与评论的实时情感分析及销售预测模型

Real-Time Sentiment Analysis and Sales Prediction Model of User Bullet Comments and Reviews in E-Commerce

摘要: 随着直播电商行业的快速发展，实时把握用户情感动态并准确预测销售趋势成为行业核心需求。本研究聚焦直播电商实时场景，以用户弹幕和评论为数据基础，创新性地构建了“情感特征–销售指标”预测模型。通过自然语言处理技术对非结构化文本进行情感量化，结合时序分析方法建立动态预测体系。研究实现了对直播间用户情绪的精准捕捉，解决了传统分析方法在实时性、准确性方面的不足。模型可为商家提供话术调整、库存管理等决策支持，助力平台优化推荐机制，填补了直播场景下情感与销售关联预测的研究空白。

Abstract: With the rapid development of the live-streaming e-commerce industry, real-time grasp of users’ emotional dynamics and accurate prediction of sales trends have become core needs of the industry. This study focuses on the real-time scenario of live-streaming e-commerce, taking users’ bullet comments and reviews as the data foundation, and innovatively constructs an “emotional feature-sales indicator” prediction model. It quantifies the emotions of unstructured texts through natural language processing technology and establishes a dynamic prediction system combined with time series analysis methods. The research realizes the accurate capture of users’ emotions in live broadcast rooms and solves the shortcomings of traditional analysis methods in terms of real-time performance and accuracy. The model can provide decision support for merchants, such as speech adjustment and inventory management, help platforms optimize recommendation mechanisms, and fill the research gap in the correlation prediction between emotions and sales in live-streaming scenarios.