A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research
ISSN: 0737-8831
Article publication date: 10 May 2022
Issue publication date: 1 June 2023
Abstract
Purpose
How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.
Design/methodology/approach
The authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.
Findings
The authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.
Originality/value
First, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.
Keywords
Acknowledgements
This study are supported by the National natural Science Foundation of China (NO. 71701172), Guangzhou Municipal Science and Technology Bureau (NO. 201907010040), Sichuan Science and Technology Program (NO. 2019YFSY0047), Research Grants Council of the Hong Kong Special Administrative Region, China (NO. 11501520). Xian Cheng is the corresponding author of the paper.
Citation
Cao, Q., Cheng, X. and Liao, S. (2023), "A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research", Library Hi Tech, Vol. 41 No. 2, pp. 543-569. https://doi.org/10.1108/LHT-03-2022-0144
Publisher
:Emerald Publishing Limited
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