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 proposed Ontology...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2021.798905/full |
| _version_ | 1818402481765351424 |
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| author | Senthil Kumar Narayanasamy Kathiravan Srinivasan Saeed Mian Qaisar Chuan-Yu Chang Chuan-Yu Chang |
| author_facet | Senthil Kumar Narayanasamy Kathiravan Srinivasan Saeed Mian Qaisar Chuan-Yu Chang Chuan-Yu Chang |
| author_sort | Senthil Kumar Narayanasamy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T08:09:03Z |
| format | Article |
| id | doaj.art-2468bb52726c48769afb574e3caae855 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-14T08:09:03Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-2468bb52726c48769afb574e3caae8552022-12-21T23:10:06ZengFrontiers Media S.A.Frontiers in Public Health2296-25652021-12-01910.3389/fpubh.2021.798905798905Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter StreamsSenthil Kumar Narayanasamy0Kathiravan Srinivasan1Saeed Mian Qaisar2Chuan-Yu Chang3Chuan-Yu Chang4School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaElectrical and Computer Engineering Department, Effat University, Jeddah, Saudi ArabiaDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, TaiwanService Systems Technology Center, Industrial Technology Research Institute, Hsinchu, TaiwanThe 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.https://www.frontiersin.org/articles/10.3389/fpubh.2021.798905/fullsentiment analysisemotion ontologynatural language processingtwitter streamslatent Dirichlet allocationSPARQL |
| spellingShingle | Senthil Kumar Narayanasamy Kathiravan Srinivasan Saeed Mian Qaisar Chuan-Yu Chang Chuan-Yu Chang Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams Frontiers in Public Health sentiment analysis emotion ontology natural language processing twitter streams latent Dirichlet allocation SPARQL |
| title | Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams |
| title_full | Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams |
| title_fullStr | Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams |
| title_full_unstemmed | Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams |
| title_short | Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams |
| title_sort | ontology enabled emotional sentiment analysis on covid 19 pandemic related twitter streams |
| topic | sentiment analysis emotion ontology natural language processing twitter streams latent Dirichlet allocation SPARQL |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2021.798905/full |
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