Predicting movies’ eudaimonic and hedonic scores: A machine learning approach using metadata, audio and visual features

Keyphrases

• Visual Features 100%
• Machine Learning Approach 100%
• H-score 100%
• Hedonic Rating 100%
• Audio Features 100%
• Eudaimonia 100%
• E-score 100%
• Metadata Features 100%
• Machine Learning Models 33%
• Audio 22%
• Feature Combination 11%
• Questionnaire Survey 11%
• Predictive Models 11%
• Automated Tool 11%
• Automatic Approach 11%
• ROC-AUC 11%
• Random Forest Classifier 11%
• Fun Experience 11%
• Modelling User Preference 11%
• Movie Recommender System 11%

Computer Science

• Visual Feature 100%
• Machine Learning Approach 100%
• Machine Learning 100%
• Learning System 100%
• Level Visual Feature 66%
• User Preference 33%
• Predictive Model 33%
• Automated Tool 33%
• Recommender Systems 33%
• Baseline Approach 33%
• Random Forest Classifier 33%
• Automatic Approach 33%

INIS

• levels 100%
• machine learning 100%
• comparative evaluations 40%
• tools 20%
• randomness 20%
• modeling 20%
• performance 20%
• datasets 20%
• depth 20%
• labelling 20%
• auc 20%
• forests 20%