社交媒体文本信息多层次细粒度属性挖掘方法研究

Research on Multi-level Fine-grained Attribute Mining Method of Social Media Text Information

【目的/意义】本文基于社交媒体文本信息,对多层次细粒度属性挖掘方法进行了研究,为准确把握人们的观点和态度提供理论和技术支撑。【方法/过程】基于基础情感极性词典以及字符情感值计算方法,对社交媒体细粒度观点挖掘体系架构进行构建,以微博话题为实证案例对细粒度观点挖掘流程及体系功能模块进行研究和验证,并进行可视化展示,结果比较简洁、清晰、容易理解,说明该方法的有效性。【结果/结论】该研究分析并改进了用户观点统计指标,验证了文中提出的细粒度观点挖掘体系框图是对传统细粒度观点挖掘模型的进一步扩展,为实践层面提升社交媒体文本信息挖掘的科学合理性提供了理论及实践支撑。

【Purpose/significance】Based on the text information of social media, this paper studies the multi-level finegrained attribute mining method, providing theoretical and technical support for accurately grasping people’s views and attitudes.【Method/process】Based on the basic emotion polarity dictionary and the calculation method of character emotion value, the fine-grained view mining system architecture of social media is constructed. The micro blog topic is taken as an empirical case to study and verify the fine-grained view mining process and system function module, and the results are relatively simple, clear and easy to understand, which shows that the method is effective Sex.【Result/conclusion】The research analyzes and improves the statistical indicators of users’ opinions, and verifies that the proposed framework of finegrained viewpoint mining system is a further expansion of the traditional fine-grained viewpoint mining model, which provides theoretical and practical support for improving the scientific rationality of social media text information mining at the practical level.