Ontology-based approach for identifying the credibility domain in social Big Data
The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academics and industry. To address this challenge, semantic analysis of textual data is focused on in this paper. We propose an ontology-based approach to extract semantics of t...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/73156 |
| _version_ | 1848762939249524736 |
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| author | Wongthongtham, Pornpit Salih, B. |
| author_facet | Wongthongtham, Pornpit Salih, B. |
| author_sort | Wongthongtham, Pornpit |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academics and industry. To address this challenge, semantic analysis of textual data is focused on in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyze the social data at two levels: the entity level and the domain level. We have chosen Twitter as a social channel for the purpose of concept proof. Ontologies are used to capture domain knowledge and to enrich the semantics of tweets, by providing specific conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification. |
| first_indexed | 2025-11-14T10:55:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-73156 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:55:32Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-731562019-02-25T06:12:27Z Ontology-based approach for identifying the credibility domain in social Big Data Wongthongtham, Pornpit Salih, B. The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academics and industry. To address this challenge, semantic analysis of textual data is focused on in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyze the social data at two levels: the entity level and the domain level. We have chosen Twitter as a social channel for the purpose of concept proof. Ontologies are used to capture domain knowledge and to enrich the semantics of tweets, by providing specific conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification. 2018 Journal Article http://hdl.handle.net/20.500.11937/73156 10.1080/10919392.2018.1517481 restricted |
| spellingShingle | Wongthongtham, Pornpit Salih, B. Ontology-based approach for identifying the credibility domain in social Big Data |
| title | Ontology-based approach for identifying the credibility domain in social Big Data |
| title_full | Ontology-based approach for identifying the credibility domain in social Big Data |
| title_fullStr | Ontology-based approach for identifying the credibility domain in social Big Data |
| title_full_unstemmed | Ontology-based approach for identifying the credibility domain in social Big Data |
| title_short | Ontology-based approach for identifying the credibility domain in social Big Data |
| title_sort | ontology-based approach for identifying the credibility domain in social big data |
| url | http://hdl.handle.net/20.500.11937/73156 |