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Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams.
Narayanasamy, Senthil Kumar; Srinivasan, Kathiravan; Mian Qaisar, Saeed; Chang, Chuan-Yu.
  • Narayanasamy SK; School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
  • Srinivasan K; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
  • Mian Qaisar S; Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia.
  • Chang CY; Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan.
Front Public Health ; 9: 798905, 2021.
Article in English | MEDLINE | ID: covidwho-1581098
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Type of study: Observational study Limits: Humans Language: English Journal: Front Public Health Year: 2021 Document Type: Article Affiliation country: Fpubh.2021.798905

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Type of study: Observational study Limits: Humans Language: English Journal: Front Public Health Year: 2021 Document Type: Article Affiliation country: Fpubh.2021.798905