Understand Research Hotspots Surrounding COVID-19 and Other Coronavirus Infections Using Topic Modeling

Mengying Dong; Xiaojun Cao; Mingbiao Liang; Lijuan Li; Huiying Liang; Guangjian Liu

The copyright holder for this preprint . https://doi.org/10.1101/2020.03.26.20044164 doi: medRxiv preprint 7 model. Increasing the number of topics would make each individual topic more specific and might increase overlap between topics. Decreasing the number of topics would result in topics to be more high-level abstract. We assigned a potential theme to each topic by manual examinations based on semantics analysis of representative words in each topic. Table 1 shows the 15 most frequent words for each of the eight topics. In most cases, topics were easily recognizable representing specific subjects about the viruses, or the disease, or the public health and so on. The first most dominant topic was enriched for the clinical characterization, with words such as 'infection', 'cause', 'disease', 'severe', 'respiratory', 'acute', 'child' and 'symptom'. Representative words of topic 2 include 'cell', 'protein', 'expression', 'bind', 'replication', 'activity' and 'membrane', which usually are . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.