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Classification du sentiment dans les critiques de films en tenant compte du contexte
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ISBN10: 6630235223
ISBN13: 9786630235227
Publisher: Editions Notre Savoir
Published: Jul 17 2026
Pages: 100
Weight: 0.32
Height: 0.24 Width: 6.00 Depth: 9.00
Language: French
ISBN13: 9786630235227
Publisher: Editions Notre Savoir
Published: Jul 17 2026
Pages: 100
Weight: 0.32
Height: 0.24 Width: 6.00 Depth: 9.00
Language: French
La croissance rapide des plateformes numériques a entraîné une augmentation considérable du volume de textes générés par les utilisateurs, en particulier les critiques de films. L'analyse manuelle de ces données s'avère difficile et chronophage. L'analyse des sentiments, un domaine important du traitement du langage naturel (TLN), permet d'identifier automatiquement les opinions exprimées dans un texte. Cette étude développe un système basé sur l'apprentissage profond afin de classer les critiques de films en positives ou négatives. Le jeu de données IMDB, composé de 50 000 critiques étiquetées, a été utilisé. Le prétraitement des données comprenait la suppression des balises HTML, la normalisation, la tokenisation, la suppression des mots vides et le stemming. Le texte traité a été converti en format numérique à l'aide d'embeddings de mots afin d'en capturer le sens. Un modèle LSTM bidirectionnel a été utilisé; celui-ci lit le texte dans les deux sens afin de mieux comprendre le contexte que les modèles traditionnels. Le modèle a été entraîné à l'aide de l'optimiseur Adam et d'une perte d'entropie croisée binaire, avec un arrêt précoce pour réduire le surapprentissage. Les résultats ont montré une précision d'environ 88,5 %, prouvant ainsi l'efficacité du modèle. Cette approche peut être appliquée aux systèmes de recommandation, à l'analyse des réseaux sociaux et à l'évaluation des retours d'expérience.
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