Learning sentiment and affects from large scale patients' narratives and biomedical documents using Neural Networks and Sentic computing: COVID-19 Case Study

Toubkal : Le Catalogue National des Thèses et Mémoires

dc.contributor.author	Grissette Hanane
dc.description.collaborator	El Beqqali, Omar (Président)
dc.description.collaborator	Idri, Ali (Rapporteur)
dc.description.collaborator	Kissi, Mohamed (Rapporteur)
dc.description.collaborator	Mahraz, Mohamed Adnane (Rapporteur)
dc.description.collaborator	Tairi, Hamid (Examinateur)
dc.description.collaborator	Zahi, Azeddine (Examinateur)
dc.description.collaborator	Boumhidi, Jaouad (Examinateur)
dc.description.collaborator	Azough, Ahmed (Examinateur)
dc.description.collaborator	Nfaoui, El Habaib (Directeur de la thèse)
dc.date.accessioned	2022-06-21T14:09:04Z
dc.date.available	2022-06-21T14:09:04Z
dc.date.issued	2021-12-11
dc.identifier.uri	http://toubkal.imist.ma/handle/123456789/15003
dc.language.iso	en	fr_FR
dc.publisher	Learning sentiment and affects from large scale patients' narratives and biomedical documents using Neural Networks and Sentic computing: COVID-19 Case Study	fr_FR
dc.relation.ispartofseries	186/2022;
dc.subject	Computer science	fr_FR
dc.subject	Sentiment analysis	fr_FR
dc.subject	Affective analysis	fr_FR
dc.subject	Sentic computing	fr_FR
dc.subject	Neural networks	fr_FR
dc.subject	Social network	fr_FR
dc.subject	Patient narrative	fr_FR
dc.title	Learning sentiment and affects from large scale patients' narratives and biomedical documents using Neural Networks and Sentic computing: COVID-19 Case Study	fr_FR
dc.description.laboratoire	Informatique, Signaux, Automatique et Cognitivisme (LISAC), (LAB.)	fr_FR

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