基于潜在主题的知识组合分析研究——以传播学为例  

Research on Knowledge Combination Analysis Based on Latent Topics—An Example of Communication

作　　者：周娜[1] 李秀霞[1] 高丹[1] 焦红 ZHOU Na;LI Xiuxia;GAO Dan;JIAO Hong(Qufu Normal University,Shandong Rizhao 276826,China)

机构地区：[1]曲阜师范大学,山东日照276826

出　　处：《农业图书情报学刊》2018年第9期85-90,共6页Journal of Library and Information Sciences in Agriculture

基　　金：国家社会科学基金项目"文献内容分析与引文分析融合的知识挖掘与发现研究"(项目编号:16BTQ074)

摘　　要：随着信息科学和通信技术的迅猛发展,传播学生成和积累了大量的数据与信息。近年来传播学发展速度很快,其研究方向及主题也较为丰富,它的学科发展脉络及热点研究主题等特征迫切需要得到系统而直观的分析及展示。为了更为全面地探索和发现研究领域的热点主题和受欢迎的"主旨-方法"对,文章提出基于分类视角的LDA主题抽取方法。以传播学领域期刊文献为研究对象,利用LDA主题模型对文献集进行主题抽取,得到30个热点主题,将主题分为两类:主旨与方法,通过深入分析各"主旨-方法"对,发现热点主题所揭示的知识点。实验发现,基于分类视角的LDA主题抽取方法能够较为全面、细致地挖掘研究领域的学科主题和研究热点。With the rapid development of information science and communication technology, communication has generated and accumulated a large amount of data and information. In recent years, the development of communication science is rapid with abundant research direction and subjects, but its subject development context and hot research topics urgently require systematic and intuitive analysis and display. In order to help explore and reveal the hot topics and popular subject-method in this field, this paper proposed LDA（ Latent Dirichlet Allocation） topic extraction method based on the perspective of classification. Taking the the periodical literature on communication field as the research objects, it used LDA topic model to extract the topic of classified literature collections and divided the selected 30 hot topics into two categories： subjects and methods. Through the deep analysis of subject-method, it revealed the knowledge point of hot topics. Experimental results indicated that this method can detect discipline theme and research hot spots in a more comprehensive and in-depth way.

关 键 词：传播学 “主旨-方法”网络 LDA模型 文本挖掘 

分 类 号：G353.1[文化科学—情报学] G210