DREAM INTERPRETATION IN ARTIFICIAL INTELLIGENCE: AN NLP AND DEEP LEARNING APPROACH

Abstract

For over a century, dream interpretation has been a central part of psychological inquiry, yet the analysis itself has stayed largely subjective and dependent on the clinician. This paper introduces an AI system for dream interpretation that combines Natural Language Processing (NLP), deep learning, and knowledge graph technology to extract psychological meaning from dream narratives automatically. A BERT (Bidirectional Encoder Representations from Transformers) model, fine-tuned for semantic encoding and sentiment analysis, is paired with a GPT-4 based generative decoder that writes interpretations rich in context. A curated dream symbol knowledge graph, which encodes Freudian and Jungian symbolic associations, acts as a structured prior that guides the interpretation. We evaluated the system on the DreamBank corpus and on a custom annotated dataset of 2,500 dream reports. It reached an interpretation accuracy of 84.6% and a semantic coherence score of 0.81 (BERTScore F1), ahead of six baseline approaches that include rule based systems, SVM classifiers, and LSTM based models. Emotion detection across seven affective categories averaged an F1 of 0.79, and each dream narrative was processed in 0.8 seconds. We discuss applications in mental health monitoring, psychological research, and digital wellness, along with the ethical questions around privacy and clinical validation.