Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1016/j.paid.2021.110701 http://scihub22266oqcxt.onion/10.1016/j.paid.2021.110701 33536695!7843112!33536695     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pers+Individ+Dif 2021 ; 175 (ä): 110701 Nephropedia Template TP {{tp|p=33536695|t=2021. Public attention about COVID-19 on social media: An investigation based on data mining and text analysis |pdf=|usr=}}{{33536695}} gab.com Text Public attention about COVID-19 on social media: An investigation based on data mining and text analysis JO: Pers Individ Dif http://www.kidney.de/pget.php?&tw=1&pmid=33536695 #FOAvip The COVID-19 epidemic is influencing global population. Social media has become important platforms to acquire and exchange information during the outbreak of COVID-19. This study explores public attention on social media. Popular Weibo texts related to COVID-19 with "coronavirus" and "pneumonia" as the keywords during December 27, 2019 and May 31, 2020 were collected in our study for public attention analysis. By combining data mining and text analysis, the public attention level trend in different stages were presented. Then a correlation analysis between public attention level and COVID-19 related cases number, topic analysis, and sentiment analysis were conducted. Significant positive correlation between public attention level and COVID-19 related cases number was identified. Based on Latent Dirichlet Allocation model, topic extraction was implemented in different stages and 41 topics were identified totally. For a comprehensive understanding of public emotions, sentiment analysis was performed. This study provides valuable lessons for public response to COVID-19. Twit Text FOAVip Public attention about COVID-19 on social media: An investigation based on data mining and text analysis ????Pers Individ Dif http://www.kidney.de/pget.php?&tw=1&pmid=33536695 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33536695 #FOAvip English Wikipedia <ref>{{Cite pmid|33536695}}</ref> Public attention about COVID-19 on social media: An investigation based on data mining and text analysis #MMPMID33536695 Hou K; Hou T; Cai L Pers Individ Dif 2021[Jun]; 175 (ä): 110701 PMID33536695show ga The COVID-19 epidemic is influencing global population. Social media has become important platforms to acquire and exchange information during the outbreak of COVID-19. This study explores public attention on social media. Popular Weibo texts related to COVID-19 with "coronavirus" and "pneumonia" as the keywords during December 27, 2019 and May 31, 2020 were collected in our study for public attention analysis. By combining data mining and text analysis, the public attention level trend in different stages were presented. Then a correlation analysis between public attention level and COVID-19 related cases number, topic analysis, and sentiment analysis were conducted. Significant positive correlation between public attention level and COVID-19 related cases number was identified. Based on Latent Dirichlet Allocation model, topic extraction was implemented in different stages and 41 topics were identified totally. For a comprehensive understanding of public emotions, sentiment analysis was performed. This study provides valuable lessons for public response to COVID-19. äPublic attention about COVID-19 on social media: An investigation base(epmc).pdf DeepDyve Pubget Overpricing