Machine Learning-Based Detection of Character Emotions and Psychological States in Literary Narratives

Abstract

The representation of emotions and psychological states is central to literary narratives, yet identifying such states systematically across large collections of fiction remains a challenging task for traditional literary criticism. Recent developments in Natural Language Processing (NLP), machine learning (ML), deep learning, and transformer-based language models provide new possibilities for computationally analyzing character emotions, affective transitions, and psychologically significant narrative patterns. This study proposes a machine-learning-based framework for detecting and analyzing character emotions and psychological states in English literary narratives. The proposed framework integrates character identification, co reference resolution, dialogue attribution, contextual emotion classification, sentiment analysis, psychological-state modeling, and temporal tracking of emotional trajectories. Unlike conventional sentiment analysis, which generally classifies the emotional polarity of a text segment, the proposed approach attempts to identify who experiences an emotion, what emotion is expressed, in what narrative context, and how that emotional state changes over time. The proposed methodology treats psychological-state detection as a computationally assisted interpretive task rather than as clinical diagnosis. It distinguishes textual evidence of states such as fear, grief, anxiety, loneliness, hope, anger, guilt, and emotional conflict from medical or psychiatric diagnosis. A research design is presented using annotated literary passages, transformer-based classification; explain ability techniques, and character-level temporal modeling. Illustrative examples demonstrate how the framework can identify emotional transitions in fictional characters. The paper contributes a multidisciplinary bridge between computational literary studies, NLP, machine learning, and affective literary criticism and provides a reproducible framework for future empirical research.