Leveraging Machine Learning for Automated Semantic Analysis in Novel Writing: Bridging Literature and Computational Linguistics

The paper will attempt to find a solution to how machine learning can be used in the semantic analysis of novel writing, as this is a way to merge the difference between literary creativeness and computational linguistics. Based on the use of the state of art unsupervised and supervised learning algorithms we examine narrative consistency, characterisation, and theme consistency as aspects that can be analysed in literary works. Rather than depending on conventional natural language processing pipelines, the proposed research focuses on graph-based machine learning and transformer-based context representation as well as vector representation to derive semantic models. The aim is to assist authors in learning how to perfect a plot and enrich its semantics, not losing artistic freedom. The given framework provides real-time feedback, identification of stylistic patterns and anomalies in plot development which is a significant contribution in literature-sensitive machine intelligence.