MoodTales: AI-Based Emotion Detection and Story Generation

Automated story generation using deep learning and transformer-based language models can produce structurally coherent narratives, but it often fails to maintain emotional accuracy and consistency, which reduces reader engagement. This research proposes an emotion-aware narrative generation framework that applies emotion classification to detect emotional states in story segments and uses this information to guide the generation of subsequent content. By integrating emotion-driven constraints into the narration process, the model ensures emotional continuity, realistic emotional transitions, and stronger emotional depth. Experimental results show that the proposed approach improves emotional coherence and overall narrative quality compared to conventional automated storytelling methods, emphasizing the importance of incorporating emotional intelligence into narrative generation systems.