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10.1007/s42979-021-00625-5 http://scihub22266oqcxt.onion/10.1007/s42979-021-00625-5 33907735!8061158!33907735     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       SN+Comput+Sci 2021 ; 2 (3): 227 Nephropedia Template TP {{tp|p=33907735|t=2021. Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification |pdf=|usr=}}{{33907735}} gab.com Text Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification JO: SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33907735 #FOAvip In present modern era, the outbreak of COVID-19 pandemic has created informational crisis. The public sentiments collected from different reflexions (hashtags, comments, tweets, posts of twitter) are measured accordingly, ensuring different policy decisions and messaging are incorporated. The implementation demonstrates intuition in to the advancement of fear sentiment eventually as COVID-19 approaches maximum levels in the world, by making use of detailed textual analysis with the help of required text data visualization. In addition, technical outline of machine learning stratification approaches are provided in the frame of text analytics, and comparing their efficiency in stratifying coronavirus tweets of different lengths. Using Naive Bayes method, 91% accuracy is achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for short tweets. Twit Text FOAVip Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification ????SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33907735 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33907735 #FOAvip English Wikipedia <ref>{{Cite pmid|33907735}}</ref> Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification #MMPMID33907735 Ramya BN; Shetty SM; Amaresh AM; Rakshitha R SN Comput Sci 2021[]; 2 (3): 227 PMID33907735show ga In present modern era, the outbreak of COVID-19 pandemic has created informational crisis. The public sentiments collected from different reflexions (hashtags, comments, tweets, posts of twitter) are measured accordingly, ensuring different policy decisions and messaging are incorporated. The implementation demonstrates intuition in to the advancement of fear sentiment eventually as COVID-19 approaches maximum levels in the world, by making use of detailed textual analysis with the help of required text data visualization. In addition, technical outline of machine learning stratification approaches are provided in the frame of text analytics, and comparing their efficiency in stratifying coronavirus tweets of different lengths. Using Naive Bayes method, 91% accuracy is achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for short tweets. äSmart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Twee(epmc).pdf DeepDyve Pubget Overpricing