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dc.contributor.authorAOUCI, NARIMENE-
dc.contributor.authorHAMIDI, NESRINE-
dc.date.accessioned2023-10-19T07:48:40Z-
dc.date.available2023-10-19T07:48:40Z-
dc.date.issued2023-07-11-
dc.identifier.urihttp://dspace.univ-tiaret.dz:80/handle/123456789/13457-
dc.description.abstractOpinion mining or sentiment analysis has emerged as a dynamic field within natural language processing, focusing on the computational analysis of sentiments and emotions expressed in written human languages. In recent years, sentiment analysis has gained significant traction across various domains including politics, production, services, marketing and others. This interest stems from the understanding that opinions hold substantial impact and contribute to decision-making processes across these domains. In this study, our main objective is to develop a system that performs sentiment analysis on Arabic texts, specifically focusing on the Algerian dialect. The system aims to analyze the extracted sentiments and classify them into positive, negative, or neutral classes using machine learning techniques and deep learning algorithmsen_US
dc.language.isoenen_US
dc.publisherIbn Khaldoun Universityen_US
dc.subjectopinion miningen_US
dc.subjectneural networken_US
dc.subjectmachine learningen_US
dc.subjectsentiment analysisen_US
dc.titleOpinion Analysis from Arabic Texts: A Case Study of the Algerian Dialect Commentsen_US
dc.typeThesisen_US
Collection(s) :Master

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