Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions
The wealth of Social Big Data (SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Conference Paper |
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2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/77681 |
| _version_ | 1848763893407547392 |
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| author | Abu Salih, Bilal Bremie, B. Clark, Ponnie Duan, K. Issa, Tomayess Chan, Kit Yan Alhabashneh, M. Albtoush, T. Alqahtani, S. Alqahtani, A. Alahmari, M. Alshareef, N. Albahlal, A. |
| author_facet | Abu Salih, Bilal Bremie, B. Clark, Ponnie Duan, K. Issa, Tomayess Chan, Kit Yan Alhabashneh, M. Albtoush, T. Alqahtani, S. Alqahtani, A. Alahmari, M. Alshareef, N. Albahlal, A. |
| author_sort | Abu Salih, Bilal |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The wealth of Social Big Data (SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application’s domain. This paper lays the theoretical background by introducing the state-of-the-art literature review of the research topic. This is associated with a critical evaluation of the current approaches, and fortified with certain recommendations indicated to bridge the research gap. |
| first_indexed | 2025-11-14T11:10:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-77681 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:10:42Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-776812021-01-28T07:17:01Z Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions Abu Salih, Bilal Bremie, B. Clark, Ponnie Duan, K. Issa, Tomayess Chan, Kit Yan Alhabashneh, M. Albtoush, T. Alqahtani, S. Alqahtani, A. Alahmari, M. Alshareef, N. Albahlal, A. cs.SI cs.SI cs.LG The wealth of Social Big Data (SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application’s domain. This paper lays the theoretical background by introducing the state-of-the-art literature review of the research topic. This is associated with a critical evaluation of the current approaches, and fortified with certain recommendations indicated to bridge the research gap. 2019 Conference Paper http://hdl.handle.net/20.500.11937/77681 10.1007/978-3-030-15035-8_87 fulltext |
| spellingShingle | cs.SI cs.SI cs.LG Abu Salih, Bilal Bremie, B. Clark, Ponnie Duan, K. Issa, Tomayess Chan, Kit Yan Alhabashneh, M. Albtoush, T. Alqahtani, S. Alqahtani, A. Alahmari, M. Alshareef, N. Albahlal, A. Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions |
| title | Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions |
| title_full | Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions |
| title_fullStr | Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions |
| title_full_unstemmed | Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions |
| title_short | Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions |
| title_sort | social credibility incorporating semantic analysis and machine learning: a survey of the state-of-the-art and future research directions |
| topic | cs.SI cs.SI cs.LG |
| url | http://hdl.handle.net/20.500.11937/77681 |