Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets

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Standort: Deutsche Nationalbibliothek Frankfurt am Main

Umfang: 1 Online-Ressource.

Sprache: Englisch

Erschienen in: Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets ; volume:13 ; number:1 ; day:9 ; month:4 ; year:2024 ; pages:1-21 ; date:12.2024
Journal of Cloud Computing ; 13, Heft 1 (9.4.2024), 1-21, 12.2024

Urheber: Abdelhady, Naglaa
Hassan A. Soliman, Taysir
F. Farghally, Mohammed

Beteiligte Personen und Organisationen: SpringerLink (Online service)

DOI: 10.1186/s13677-024-00644-6

URN: urn:nbn:de:101:1-2406241210078.938765241089

Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Letzte Aktualisierung: 14.08.2025, 10:59 MESZ

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Abdelhady, Naglaa
Hassan A. Soliman, Taysir
F. Farghally, Mohammed
SpringerLink (Online service)

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Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets

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