Extraction d'un vocabulaire de surprise par mélange de filtrage collaboratif et d'analyse de sentiments

L'informatique subit actuellement une mutation profonde: les ameliorations mate- rielles et les grandes quantites de donnees disponibles fournissent un terrain fertile a la re- cherche en apprentissage automatique. Dans ce contexte, le principal defi est de tenir compte des preferences des utilisateurs pour proposer un acces personnalise a l'information. Les sys- temes de recommandation creent des profils utilisateurs et objets en utilisant les revues utilisa- teurs, et ces profils refletent les preferences des utilisateurs et les caracteristiques des objets. Nous proposons ici une analyse par combinaison de systemes de recommandation et classifieurs de polarite qui met en evidence le vocabulaire de la surprise. En effet, la recommandation ana- lyse le passe et anticipe les attentes d'un utilisateur tandis que le classifieur de polarite prend en entree une revue deja ecrite par l'utilisateur: nous montrons que l'ecart entre l'experience attendue et le retour reel sur un objet permet de construire un lexique de la surprise.

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