Discovering research topics from library electronic references using latent Dirichlet allocation
作者: Debin FangHaixia YangBaojun GaoXiaojun Li
作者单位: 1Economics and Management School, Wuhan University , Wuhan, China
2College of Accounting, Yunnan University of Finance and Economics , Kunming, China
刊名: Library Hi Tech, 2018, Vol.36 (3), pp.400-410
来源数据库: Emerald Journal
DOI: 10.1108/LHT-06-2017-0132
关键词: Academic librariesBig dataAccounting researchLatent Dirichlet allocation (LDA)Topic modelTopic trends
原始语种摘要: Purpose(#br)Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms. (#br)Design/methodology/approach(#br)The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. (#br)Findings(#br)First,...
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关键词翻译
关键词翻译
  • allocation 分配
  • latent 潜在的
  • research 
  • electronic 电子的
  • methodology 方法学
  • references 参考文献
  • accounting 会计
  • library 图书馆
  • quantity 
  • probabilistic 概率的