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• W4381747418 abstract "There are a variety of social media platforms where people can review products and services depending on personal expertise. The user’s friends and followers may see each other’s ideas and sentiments, which may spread to more users in the future. Therefore, this research proposes a concept named “sentiment community”. The purpose is to explore the feelings and interactions of users on social networking sites. We have used graphs for the modelling of these networks. The suggested sentiment community discovery approach alters the original graph based on sentiment scores of users. Experiments have been conducted on networks generated using the LFR model and Twitter’s data. In the experiments, the modularity score is used to determine the quality of the detected community structure. The communities were detected in each network with and without the sentiment concept. The results reveal that the inclusion of the sentiment score concept for community detection significantly improves the modularity score and quality of the resultant community structure." @default.
• W4381747418 created "2023-06-24" @default.
• W4381747418 creator A5013882973 @default.
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• W4381747418 date "2023-01-01" @default.
• W4381747418 modified "2023-09-25" @default.
• W4381747418 title "Sentiment-Based Community Detection Using Graph Transformation" @default.
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• W4381747418 doi "https://doi.org/10.1007/978-981-99-0981-0_6" @default.
• W4381747418 hasPublicationYear "2023" @default.
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