Digital Enhancement of Literary Analysis: Machine Learning in Textual Criticism

Thesis Abstract

The rapid advancement of digital technologies has transformed traditional approaches to literary analysis, enabling scholars to leverage machine learning algorithms for enhanced textual criticism. This study investigates the application of machine learning techniques in the analysis of literary texts, aiming to develop a digital framework that enhances accuracy and efficiency in textual criticism. The central problem addressed is the limited capacity of manual methods to handle large corpora and detect subtle textual variations, which hampers comprehensive literary analysis. The specific objectives are to (1) evaluate the effectiveness of machine learning models, particularly supervised learning algorithms such as random forests and support vector machines, in identifying textual variants; (2) develop a prototype digital tool capable of automating aspects of textual collation; and (3) assess the interpretability and scholarly acceptance of machine-driven findings compared to traditional methods. The research adopts a mixed-methods approach, combining quantitative machine learning model evaluation with qualitative interpretative analysis. The population comprises 50 literary texts, including canonical works by Shakespeare, Dickens, and Goethe, selected based on availability of established critical variants. A sample of 10 texts is used for training and tuning machine learning models, while the remaining 40 texts serve for validation and case studies. Data collection involves digital transcription of texts, followed by manual annotation of textual variants to generate labeled datasets. Additional data are collected through expert feedback interviews on the interpretability of machine-generated results. The primary data analysis employs supervised learning techniques such as random forests, support vector machines, and neural networks to classify and cluster textual variants. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. A thematic analysis is conducted on expert feedback to assess the practical interpretability of machine learning outputs. The study utilizes the Theory of Textual Variance as a guiding framework, complemented by computational linguistics principles, to interpret the relationship between automated classifications and traditional philological insights. Expected findings indicate that machine learning models, particularly ensemble methods like random forests, outperform baseline algorithms in detecting subtle textual variants, achieving accuracy rates above 85%. The developed digital tool demonstrates potential in reducing manual labor while maintaining or exceeding manual accuracy levels. Preliminary qualitative analyses suggest that interpretability varies across models; transparent algorithms such as decision trees are favored by scholars for their explainability, whereas deeper neural networks require supplementary visualization tools to enhance interpretability. The findings also reveal distinctive patterns in textual variance that align with conventional critical observations, validating the models' scholarly relevance. This research significantly contributes to the field of literary studies by formalizing a replicable, technology-driven methodology for textual criticism, bridging computational methods and philological expertise. It advances theoretical understanding of machine learning applicability in humanities research, particularly under the framework of Textual Variance Theory, and provides practical tools for scholars seeking to integrate digital methods into their workflow. The study recommends wider adoption of machine learning techniques in textual criticism workflows, emphasizing the importance of interpretability and scholar collaboration in model development. It also suggests avenues for future research, including expanding to multilingual corpora and exploring unsupervised learning models for discovering previously unrecognized textual patterns. Overall, the study underscores the transformative potential of digital innovations in enriching literary analysis and advancing rigorous, scalable, and replicable critical methodologies.