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.