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.

suck abstract from ncbi


10.3389/fpubh.2021.798905

http://scihub22266oqcxt.onion/10.3389/fpubh.2021.798905
suck pdf from google scholar
34938715!8685242!34938715
unlimited free pdf from europmc34938715    free
PDF from PMC    free
html from PMC    free
PDF vom PMID34938715 :   free

suck abstract from ncbi

pmid34938715
Nephropedia Template TP

gab.com Text

Twit Text FOAVip

Twit Text #

English Wikipedia


  • 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]
  • |Sentiment Analysis[MESH]


  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    798905 ä.9 2021