Dreams Through the Lens of AI: Comparative Insights into Emotion Prediction
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT October 23, 2025 Devinder Kumar, Jwala Jose
Gradient Boosting outperforms Random Forest, Support Vector Machines, and Neural Networks in predicting emotions such as happiness, sadness, fear, and anger from dream reports. Using a dataset of 5,000 labeled dream reports, the models were evaluated on accuracy, precision, recall, F1-score, and AUC-ROC. Gradient Boosting achieved the highest accuracy and AUC-ROC values, making it the most effective model for emotion prediction in dreams. The findings indicate that AI can offer insights into the subconscious mind and has potential applications in psychology and mental health.