吉林大学学报（理学版）2025，Vol.63Issue(2)：528-536,9.DOI:10.13413/j.cnki.jdxblxb.2023475
基于动态主题情感模型的文本聚类算法
Text Clustering Algorithm Based on Dynamic Theme Emotion Model
摘要
Abstract
Aiming at the problem that the emotional factors of the public were not considered enough in the existing related theme models,which was difficult to accurately excavate them,and the real-time dynamic evolution of social texts was considered to weaken the clustering ability of the model,the author proposed a text clustering algorithm based on the dynamic theme emotin model by adding the emotional layer to the model to extract the polar features of social text emotion,and introducing a prior distribution function.The experiments were carried out by using real COVID-19 Twitter text datasets.The experimental results show that the performance of the model is better than the baseline model,and the discrimination of emotional features is improved,so that the text theme and the corresponding emotional polarity can jointly generate time nodes,and then the model has the ability to deal with time evolution.关键词
动态主题情感模型/文本挖掘/情感标签/时间戳/文本聚类/困惑度Key words
dynamic topic emotion model/text mining/emotional label/time stamp/text clustering/perplexity分类
信息技术与安全科学引用本文复制引用
胡萍..基于动态主题情感模型的文本聚类算法[J].吉林大学学报（理学版）,2025,63(2):528-536,9.基金项目
国家自然科学基金面上项目(批准号:62066040)、教育部人文社科青年基金(批准号:20YJC880030)和铜仁学院博士科研启动基金(批准号:trxyDH1914). （批准号:62066040）
