Artificial Intelligence and its Impact on English Literature

Abstract

This study examines the promises and limitations of Artificial Intelligence in the field of literary analysis by applying Natural Language Processing tools to selected passages from Chinua Achebe’s Things Fall Apart. It focuses particularly on sentiment and emotion detection systems and evaluates how effectively such tools interpret complex literary language shaped by culture, character psychology, and narrative context. By analysing key excerpts involving the character of Okonkwo, the study compares automated emotional readings with close human literary interpretation. The findings reveal noticeable inconsistencies between algorithmic outputs and contextual meaning, especially where cultural references, figurative language, and character temperament are involved. While AI systems successfully identify surface-level emotional patterns in some instances, they frequently misinterpret deeper narrative cues, symbolic references, and culturally embedded expressions. The study argues that literary meaning is not solely contained in words but in the beliefs, contexts, and interpretive frameworks that surround them. It highlights the challenges of training AI models to understand culturally specific narratives and demonstrates that without contextual learning, such systems risk oversimplifying or distorting literary meaning. Ultimately, the study positions AI as a supportive analytical aid rather than a replacement for human literary judgment.