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arXiv · 2503.05778

DreamNet: A Multimodal Framework for Semantic and Emotional Analysis of Sleep Narratives

Tapasvi Panchagnula
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Abstract

Dream narratives provide a unique window into human cognition and emotion, yet their systematic analysis using artificial intelligence has been underexplored. We introduce DreamNet, a novel deep learning framework that decodes semantic themes and emotional states from textual dream reports, optionally enhanced with REM-stage EEG data. Leveraging a transformer-based architecture with multimodal attention, DreamNet achieves 92.1% accuracy and 88.4% F1-score in text-only mode (DNet-T) on a curated dataset of 1,500 anonymized dream narratives, improving to 99.0% accuracy and 95.2% F1-score with EEG integration (DNet-M). Strong dream-emotion correlations (e.g., falling-anxiety, r = 0.91, p < 0.01) highlight its potential for mental health diagnostics, cognitive science, and personalized therapy. This work provides a scalable tool, a publicly available enriched dataset, and a rigorous methodology, bridging AI and psychological research.

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Tapasvi Panchagnula. 2025-02-26. DreamNet: A Multimodal Framework for Semantic and Emotional Analysis of Sleep Narratives. https://arxiv.org/abs/2503.05778

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