基于lda的微博用户兴趣标签提取系统的设计与实现

随着社交网络平台的盛行，微博作为社交用户的交流平台，其用户发表的文本内容具有很高的研究价值，通过从用户发表的微博内容中提取微博用户的兴趣标签，可便于企业向用户提供信息推荐和个性化信息展示服务。LDA模型是一种对离散数据集 (如文档集) 建模的概率主题模型，可以用于微博主题信息提取。因此，基于LDA的微博用户兴趣标签提取系统开发具有一定的实践意义。本系统采用UML用例图进行功能分析，E-R图进行数据分析。在需求分析的基础上，基于Django框架进行系统架构设计和模块设计。最后基于Django开发环境实现了系统的数据获取、数据预处理、数据管理、标签提取等功能。

关键词：Python；Django；LDA；MySQL数据库；微博；兴趣标签；

Design and Implementation of Microblog User Interest Label Extraction System Based on LDA

Abstract：With the prevalence of social networking platforms, Weibo has high research value for texts published by users as a communication platform for social users. By extracting the interest tags of Weibo users from the Weibo content published by users, it is convenient for enterprises to provide information recommendation and personalized information display services to users. The LDA model is a probabilistic topic model that models discrete data sets (such as document sets) and can be used for microblog topic information extraction. Therefore, it is practical significant to develop LDA-based Weibo user interest tag extraction system. UML use case diagram is used for functional analysis and E-R diagram is used for data analysis. Based on the requirements analysis, the system architecture design and module design are based on the Django framework. Finally, based on the Django development environment.The system function of data acquisition,data preprocessing, data management, and tag extraction are realized.

Keywords: Python; Django; LDA; MySQL database; Weibo; Interest tag;

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