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10.3389/fpubh.2021.798905 http://scihub22266oqcxt.onion/10.3389/fpubh.2021.798905 34938715!8685242!34938715     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\34938715.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Front+Public+Health 2021 ; 9 (ä): 798905 Nephropedia Template TP {{tp|p=34938715|t=2021. Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams |pdf=|usr=}}{{34938715}} gab.com Text Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=34938715 #FOAvip The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of "web of data". In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches. Twit Text FOAVip Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=34938715 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34938715 #FOAvip English Wikipedia <ref>{{Cite pmid|34938715}}</ref> Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams #MMPMID34938715 Narayanasamy SK; Srinivasan K; Mian Qaisar S; Chang CY Front Public Health 2021[]; 9 (ä): 798905 PMID34938715show ga The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of "web of data". In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches. |*COVID-19[MESH] |*Social Media[MESH] |Emotions[MESH] |Humans[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH]Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Rel(epmc).pdf DeepDyve Pubget Overpricing