融合语义关联和文本降维的社交媒体主题提取  

Social Media Topic Extraction Based on Semantic Association and Text Dimension Reduction

作　　者：彭云[1] 万红新 PENG Yun;WAN Hongxin(School of Computer and Information Engineering,Jiangxi Normal University,Nanchang Jiangxi 330022,China;School of Mathematics and Computer Science,Jiangxi Science and Technology Normal University,Nanchang Jiangxi 330038,China)

机构地区：[1]江西师范大学计算机信息工程学院,江西南昌330022 [2]江西科技师范大学数学与计算机科学学院,江西南昌330038

出　　处：《信息与电脑》2021年第11期183-185,共3页Information & Computer

基　　金：江西省高校人文社科项目(项目编号:JC19121);江西省教育厅科技项目(项目编号:GJJ201127)。

摘　　要：随着互联网的普及,社交媒体平台上积累了大量的文本数据,并逐渐形成社交媒体文本大数据。这些文本数据的语法和语义结构复杂,需要运用数据挖掘、自然语言处理等相关技术提取关键词。基于此,笔者提出融合语义关联知识和文本降维模型的社交媒体主题提取模型。实验表明,该模型对复杂网络文本数据的主题提取具有较好的性能。With the popularity of the Internet,a large amount of text data has accumulated on the social media platform,and gradually formed social media text big data.The syntax and semantic structure of these text data are complex,so it is necessary to use data mining,natural language processing and other related technologies to extract keywords.Based on this,this paper proposes a social media topic extraction model which integrates semantic association knowledge and text dimension reduction model.Experiments show that the model has good performance for topic extraction of complex network text data.

关 键 词：语义关联 主题模型 社交媒体 文本降维 

分 类 号：G206[文化科学—传播学] TP311[自动化与计算机技术—计算机软件与理论]