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Enhanced sentiment extraction architecture for social media content analysis using capsule networks

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dc.contributor.author Demotte, P
dc.contributor.author Wijegunarathna, K
dc.contributor.author Meedeniya, D
dc.contributor.author Perera, I
dc.date.accessioned 2023-12-01T08:12:47Z
dc.date.available 2023-12-01T08:12:47Z
dc.date.issued 2023
dc.identifier.citation Demotte, P., Wijegunarathna, K., Meedeniya, D., & Perera, I. (2023). Enhanced sentiment extraction architecture for social media content analysis using capsule networks. Multimedia Tools and Applications, 82(6), 8665–8690. https://doi.org/10.1007/s11042-021-11471-1 en_US
dc.identifier.issn 1573-7721 en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21875
dc.description.abstract Recent research has produced efficient algorithms based on deep learning for text-based analytics. Such architectures could be readily applied to text-based social media content analysis. The deep learning techniques, which require comparatively fewer resources for language modeling, can be effectively used to process social media content data that change regularly. Convolutional Neural networks and recurrent neural networks based approaches have reported prominent performance in this domain, yet their limitations make them sub-optimal. Capsule networks sufficiently warrant their applicability in language modelling tasks as a promising technique beyond their initial usage of image classification. This study proposes an approach based on capsule networks for social media content analysis, especially for Twitter. We empirically show that our approach is optimal even without the use of any linguistic resources. The proposed architectures produced an accuracy of 86.87% for the Twitter Sentiment Gold dataset and an accuracy of 82.04% for the CrowdFlower US Airline dataset, indicating state-of-the-art performance. Hence, the research findings indicate noteworthy accuracy enhancement for text processing within social media content analysis. en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.subject Deep learning en_US
dc.subject Capsule networks en_US
dc.subject Twitter en_US
dc.subject Sentiment analysis en_US
dc.subject Social media content analysis en_US
dc.title Enhanced sentiment extraction architecture for social media content analysis using capsule networks en_US
dc.type Article-Full-text en_US
dc.identifier.year 2023 en_US
dc.identifier.journal Multimedia Tools and Applications en_US
dc.identifier.issue 6 en_US
dc.identifier.volume 82 en_US
dc.identifier.database Springer Link en_US
dc.identifier.pgnos 8665–8690 en_US
dc.identifier.doi https://doi.org/10.1007/s11042-021-11471-1 en_US


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