Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams

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 propose...

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Bibliographic Details
Published in:Frontiers in public health 2021-12, Vol.9, p.798905-798905
Main Authors: Narayanasamy, Senthil Kumar, Srinivasan, Kathiravan, Mian Qaisar, Saeed, Chang, Chuan-Yu
Format: Article
Language:English
Subjects:
COVID-19
emotion ontology
Emotions
Humans
latent Dirichlet allocation
natural language processing
Pandemics
Public Health
SARS-CoV-2
Sentiment Analysis
Social Media
SPARQL
twitter streams
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Summary: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.
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2021.798905