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- W3185137028 abstract "Recently so many users who are different in power/interest trade news on social networking sites like Arabic Twitter and share their views on current affairs. These opinions/comments can't be used to make good decisions or boost output in a particular area without differentiating users according to their closeness/interest to this area. This paper's major goal is to present a generalized automatic model to analyze user opinions to make valuable decisions in a particular area based on the degree of user closeness/interest to this area. The proposed model combines Rough set theory, Mendelow's power-interest model, and data mining decision-making techniques. Rough set theory based on Mendelow's power-interest model supports the identification and classification of users by their account features. Unsupervised k-means would then be used to cluster their replies/opinions into positive, negative, or neutral. The result generated from the classification of users and the clustering phase of Arabic replies/opinions supports the making of valuable/important decisions in a particular area. A case study is carried out to demonstrate the effectiveness and accuracy of the proposed model." @default.
- W3185137028 created "2021-08-02" @default.
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- W3185137028 date "2021-07-03" @default.
- W3185137028 modified "2023-10-14" @default.
- W3185137028 title "A new model for making valuable decisions through user social network profiles and insights" @default.
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- W3185137028 doi "https://doi.org/10.1109/iceem52022.2021.9480618" @default.
- W3185137028 hasPublicationYear "2021" @default.
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