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• W4386574296 abstract "Abstract Currently, a noteworthy volume of information is available and shared every day through participation and communication of individuals on social media. These enormous contents with the right exploit and research leads to valuable discoveries. In this study, a deep framework of learning accurate detection of women’s depression is proposed. It is beneficially guided by social media content of individual posts and tweets and an essential support from psycho-linguistic for providing the indicator depression signs vocabulary that creates the embedding words necessary for building the applied approach. The presented model is validated using dual datasets extracted from Twitter: the first dataset is general data formed by 700 women from different countries; the second contains only 80 women from KSA. A third benchmark dataset CLPsych 2015 is used for comparative analysis purposes. The model proved its performance on the three datasets and the obtained and reported in this paper results shows its effectiveness." @default.
• W4386574296 created "2023-09-10" @default.
• W4386574296 creator A5001478861 @default.
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• W4386574296 date "2023-09-09" @default.
• W4386574296 modified "2023-09-26" @default.
• W4386574296 title "A pre-protective objective in mining females social contents for identification of early signs of depression using soft computing deep framework" @default.
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• W4386574296 doi "https://doi.org/10.1038/s41598-023-40607-6" @default.
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• W4386574296 hasPublicationYear "2023" @default.
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