An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation

Cheng, Xian; Cao, Qiang; Liao, Stephen Shaoyi

doi:10.1177/0165551520954674

Cited by 57 publications

(44 citation statements)

References 65 publications

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“…Text mining techniques like topic modeling using LDA have been widely used to extract the research hotspots and other related information from the articles on different coronavirus-related diseases such as COVID-19, SARS, and MERS, etc. [41], [40]. Also, interesting recurring patterns have been identified by using scientometric comparisons across various coronavirus literature [39].…”

Section: A Backgroundmentioning

confidence: 97%

A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research

2021

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COVID-19 is a global health crisis that has altered human life and still promises to create ripples of death and destruction in its wake. The sea of scientific literature published over a short time-span to understand and mitigate this global phenomenon necessitates concerted efforts to organize our findings and focus on the unexplored facets of the disease. In this work, we applied natural language processing (NLP) based approaches on scientific literature published on COVID-19 to infer significant keywords that have contributed to our social, economic, demographic, psychological, epidemiological, clinical, and medical understanding of this pandemic. We identify key terms appearing in COVID literature that vary in representation when compared to other virus-borne diseases such as MERS, Ebola, and Influenza. We also identify countries, topics, and research articles that demonstrate that the scientific community is still reacting to the short-term threats such as transmissibility, health risks, treatment plans, and public policies, underpinning the need for collective international efforts towards long-term immunization and drug-related challenges. Furthermore, our study highlights several long-term research directions that are urgently needed for COVID-19 such as: global collaboration to create international open-access data repositories, policymaking to curb future outbreaks, psychological repercussions of COVID-19, vaccine development for SARS-CoV-2 variants and their long-term efficacy studies, and mental health issues in both children and elderly.

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Section: A Backgroundmentioning

confidence: 97%

A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research

2021

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“…Belli et al ( 2020 ) focus on the scientific collaboration and open access of Covid-19 research. Cheng, Cao and Liao ( 2020 ) reveal a network visualization to explore the main research themes of Covid-19. Tovstiga and Tovstiga ( 2020 ) study the Covid-19 pandemic from a knowledge and learning perspective.…”

Section: Literature Studymentioning

confidence: 99%

Research on Covid-19: a disruptive phenomenon for bibliometrics

Fassin

2021

Scientometrics

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The Covid-19 pandemic has been the highest disruptive event in the world recent history. Worldwide academic research on this topic has led to an explosion of scientific literature, never seen before. Bibliometrics provide methods to illustrate this exceptional phenomenon in academic publications. The objective of this paper is to analyze the Covid-19 research from a bibliometric perspective and to study the impact of the publication explosion on bibliometric indicators. The present study shows how an exceptional phenomenon has a disruptive impact on bibliometric indicators, such as the h-index and the Journal Impact Factor. The higher the specialization, the higher the possible impact of a disruptive phenomenon. In applied sciences, more important than the research or the discipline, the specific theme of the research is crucial for citations of articles and for their impact. The salience of the topic, the magnitude of the problem at study and the urgency to find solutions are drivers for citations. The study of the Covid-19 research illustrates the relativity of indicators and the need for context. The present study also confirms the plead for responsible metrics of the San Francisco Declaration on Research Assessment (DORA).

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“…In another research, the topics of global publications on COVID-19 in the CORD-19 were identified using text mining and topic modeling techniques; this study revealed that the COVID-19 publications have focused more on and have paid less attention to which topics [35]. In another study, the publications related to SARS, MERS, and COVID-19 were evaluated; in this article, the text mining of topics was performed separately, the scientific publications related to each of the viruses were modeled independently, and finally, the results were reviewed using an analytical-comparative approach [36]. In another paper, COVID-19 publications were extracted in the first six months of the pandemic using PubMed, and the topics of COVID-19 articles and their publication trends were extracted using topic modeling and text mining techniques [37].…”

Section: Introductionmentioning

confidence: 99%

Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

Dastani

Danesh

2021

Scientific Programming

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COVID-19 is a threat to the lives of people all over the world. As a result of the new and unknown nature of COVID-19, much research has been conducted recently. In order to increase and enhance the growth rate of Iranian publications on COVID-19, this article aims to analyze these publications in LitCovid to identify the topical and content structure and topic modeling of scientific publications in the mentioned subject area. The present article is applied research performed by using an analytical approach as well as text mining techniques. The statistical population is all the publications of Iranian researchers in LitCovid. Latent Dirichlet Allocation (LDA) and Python were used to analyze the data and implement text mining and topic modeling algorithms. Data analysis shows that the percentage of Iranian publications in the eight topical groups in LitCovid is as follows: prevention (39.57%), treatment (18.99%), diagnosis (18.99%), forecasting (7.83%), case report (6.52%), mechanism (3.91%), transmission (3.62%), and general (0.58%). The results indicate that patient, pandemic, outbreak, case, Iranian, model, care, health, coronavirus, and disease are the most important words in the publications of Iranian researchers in LitCovid. Six topics for prevention; four topics for treatment and case report and forecasting; three topics for diagnosis, mechanism, and transmission in general have been obtained by implementing the topic modeling algorithm. Most of the Iranian publications in LitCovid are related to the topic “pandemic status,” with 22.47% in the prevention category, and the lowest number of publications is related to the topic “environment,” with 11.11% in the transmission category. The present study indicates a better understanding of essential and strategic issues of Iranian publications in LitCovid. The results reveal that many Iranian studies on COVID-19 were primarily on the issues related to prevention, management, and control. These findings provided a structured and research-based viewpoint of COVID-19 in Iran to guide researchers and policymakers.

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An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation

Cited by 57 publications

References 65 publications

A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research

A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research

Research on Covid-19: a disruptive phenomenon for bibliometrics

Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

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