Algorithmic Ambiguity: Using Natural Language Processing (NLP) to Identify and Map Unreliable Narration in Postmodern Novels

Abstract

The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has transformed the methods through which literary texts are analyzed. Traditionally, literary criticism has relied on close reading, subjective interpretation, and theoretical frameworks developed by scholars in literature. However, computational approaches now enable researchers to examine linguistic patterns, narrative structures, and stylistic variations on a much larger scale. Among the most intriguing applications of NLP is the identification of unreliable narration, a narrative technique frequently employed in postmodern literature to challenge readers' perceptions of truth and reality. Unreliable narrators intentionally or unintentionally provide distorted, contradictory, or incomplete accounts of events, making interpretation both intellectually demanding and artistically rewarding.
This research paper explores how NLP techniques can be employed to identify and map unreliable narration in postmodern novels. The study examines computational methods such as sentiment analysis, stylometry, semantic analysis, topic modeling, named entity recognition, and transformer-based language models to detect inconsistencies in narrative voice and perspective. Through selected examples from postmodern fiction, the paper demonstrates that algorithmic approaches can complement traditional literary criticism by identifying linguistic signals associated with ambiguity and unreliability. While algorithms cannot replace human interpretation, they provide valuable evidence that enhances literary analysis. The paper concludes that integrating computational linguistics with literary studies opens new opportunities for interdisciplinary research and contributes to the growing field of digital humanities.

Keywords