Digital Literary Analytics
Digital Literary Analytics uses intelligent computation to study literature through quantitative and qualitative lenses. AI models analyze plot structure, narrative style, character networks, rhetorical strategies, and thematic evolution across entire literary corpora. Stylometry reveals authorship patterns and textual influences. Sentiment and emotion analysis track psychological depth and narrative tone. Interdisciplinary work merges literary theory, linguistics, psychology, cultural studies, and data science. Visualization tools map narrative arcs, genre boundaries, and historical shifts in language. Computational methods enable comparative literature studies at scales previously impossible. Digital literary analytics expands interpretative possibilities and deepens understanding of human expression through text.