Discovering research topics from library electronic references using latent Dirichlet allocation

作者：Fang, Debin; Yang, Haixia; Gao, Baojun^*; Li, Xiaojun

来源：Library Hi Tech, 2018, 36(3): 400-410.

DOI：10.1108/LHT-06-2017-0132

• Academic libraries
• Big data
• Accounting research
• Latent Dirichlet allocation (LDA)
• Topic model
• Topic trends

Purpose 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.
Design/methodology/approach 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.
Findings First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics.
Originality/value The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.

• 出版日期2018
• 单位云南财经大学; 武汉大学