Digital Textual Analysis of Postcolonial Literature using Machine Learning Techniques

Thesis Abstract

The increasing corpus of postcolonial literature presents both opportunities and challenges for literary analysis, particularly in uncovering nuanced themes, linguistic patterns, and cultural subtexts that traditional manual methods may overlook or require extensive time to analyze. This study addresses the gap in systematic, scalable analytical approaches by applying advanced machine learning techniques to digitally analyzed postcolonial texts, aiming to enhance interpretive depth and methodological rigor. The primary objective is to develop a comprehensive digital analytical framework capable of automating thematic identification, stylistic differentiation, and authorship attribution within a diverse corpus of postcolonial literature. Specific objectives include evaluating the effectiveness of natural language processing (NLP) algorithms in extracting salient thematic elements, comparing supervised and unsupervised machine learning models in classifying texts based on cultural and linguistic features, and examining the applicability of these methods in revealing subtle literary devices used to critique colonial legacies. Employing a mixed-methods research design, the study integrates quantitative computational analysis with qualitative interpretive validation. The population comprises a curated corpus of 150 digitized literary works authored by prominent postcolonial writers from regions such as the Caribbean, South Asia, and Africa, spanning texts published between 1950 and 2020. A stratified random sampling technique ensures representative inclusion across different geographical and thematic categories. Data collection involves digitizing texts obtained from established literary databases and libraries, followed by preprocessing to remove noise and standardize formats. The primary instruments include a bespoke software environment integrating Python-based NLP libraries (such as spaCy and NLTK) and custom machine learning models built using scikit-learn and TensorFlow for training classifiers, clustering algorithms, and topic models. The analysis proceeds through systematic feature extraction encompassing lexical richness, semantic vectors (via word embeddings like Word2Vec and BERT), and stylistic markers. Supervised learning models, including support vector machines (SVM) and random forests, are employed for text categorization, while unsupervised models such as Latent Dirichlet Allocation (LDA) facilitate thematic discovery. Model validation involves cross-validation procedures, precision and recall metrics, and interpretability assessments to ensure robustness and contextual relevance of findings. The study also incorporates qualitative thematic analysis to interpret machine-generated outputs within the socio-political and cultural contexts of postcolonial phenomena, grounded within Edward Said’s Orientalism and Homi Bhabha’s hybridity theories. Expected findings include the identification of distinct thematic clusters associated with postcolonial resistance, cultural identity negotiation, and colonial critique, with machine learning demonstrating high accuracy (over 85%) in text classification tasks. The research anticipates revealing latent stylistic features that differentiate writers’ voices and regional perspectives, contributing novel insights into the intersection of literary form and postcolonial discourse. Furthermore, the study aims to establish that digital textual analysis complemented by machine learning enhances interpretive efficiency and objectivity, enabling scholars to process larger corpora more effectively. This research contributes to the scholarly understanding of postcolonial literature by pioneering a replicable, technology-driven analytical paradigm that complements traditional literary criticism. It underscores the potential of computational methods to uncover new patterns and relationships within texts, thereby broadening methodological horizons. The conclusions emphasize the importance of integrating digital humanities tools into postcolonial studies and recommend that future research expand the corpus scope, incorporate multilingual datasets, and explore deep-learning models for even more nuanced analysis. The findings advocate for a hybrid approach that combines computational techniques with human interpretative expertise, fostering more comprehensive and culturally sensitive understanding of postcolonial literary works.