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Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics

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Published in:Discover AI
Format: Online Article RSS Article
Published: 2026
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publishDate 2026
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spellingShingle Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
subject_facet Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
title Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
title_auth Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
title_full Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
title_fullStr Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
title_full_unstemmed Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
title_short Machine learning methods for detecting depression in Arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
title_sort machine learning methods for detecting depression in arabic tweets: a comprehensive performance analysis with enhanced evaluation metrics
topic Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
url https://link.springer.com/article/10.1007/s44163-026-00842-y