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10.1007/s11192-020-03744-7 http://scihub22266oqcxt.onion/10.1007/s11192-020-03744-7 33230352!7676411!33230352     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33230352&cmd=llinks): Failed to open stream: HTTP request failed! 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Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing |pdf=|usr=}}{{33230352}} gab.com Text Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing JO: Scientometrics http://www.kidney.de/pget.php?&tw=1&pmid=33230352 #FOAvip The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed. Twit Text FOAVip Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing ????Scientometrics http://www.kidney.de/pget.php?&tw=1&pmid=33230352 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33230352 #FOAvip English Wikipedia <ref>{{Cite pmid|33230352}}</ref> Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing #MMPMID33230352 Ebadi A; Xi P; Tremblay S; Spencer B; Pall R; Wong A Scientometrics 2021[]; 126 (1): 725-739 PMID33230352show ga The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed. äUnderstanding the temporal evolution of COVID-19 research through mach(epmc).pdf DeepDyve Pubget Overpricing