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Food recommender system for cancer patients based on sentiment analysis.

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dc.contributor.author Boughafene, Chayma
dc.contributor.author El Bouhissi Brahami, Houda ; promotrice
dc.date.accessioned 2024-12-08T10:47:40Z
dc.date.available 2024-12-08T10:47:40Z
dc.date.issued 2024
dc.identifier.other 004MAS/1307
dc.identifier.uri http://univ-bejaia.dz/dspace/123456789/24872
dc.description Option : systéme d’information avancés en_US
dc.description.abstract Cancer is a serious disease characterized by abnormal and irregular cell development in any part of the body, in the form of a tumor. It is considered as the second-leading cause of death in the world. Efforts to find a successful cancer therapy have led to the effective use of various treatments such as chemotherapy, and surgery to eliminate dangerous tumors. However, these treatments may affect the patient's immune system by damaging blood cells that protect the body from disease. Therefore, it is strongly recommended that cancer patients consume nutrient-rich foods to increase their strength to better cope with the side effects of treatment. In this dissertation, a hybrid food recommendation system which considers the patient's emotional state and dietary preferences and needs to help them predict the foods which can be consumed have been proposed. The approach involve to use a content-based system that filters recipes according to user needs and sentiment scores. Additionally, a rule-based sentiment analysis method was employed to identify sentiment from text reviews determining which of the foods were liked or disliked. The efficacy of this proposed approach was rigorously assessed, and the results yielded promising insights. Notably, combining content based, sentiments analysis led to a marked improvement with a precision of 97%. en_US
dc.language.iso en en_US
dc.publisher Université Abderramane Mira-Bejaia en_US
dc.subject Nutrition suggestion : Cancer : Sentiment analysis :Content-based filtering en_US
dc.title Food recommender system for cancer patients based on sentiment analysis. en_US
dc.type Thesis en_US


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