AI-Powered Literary Analysis Toolkit

Problem

Literary scholars and researchers studying African literature often lack computational tools tailored to their specific needs. Traditional NLP libraries are optimized for high-resource languages and modern texts, making them less effective for analyzing literary works from diverse linguistic and cultural backgrounds. Researchers need accessible tools that can handle multilingual texts and respect literary context.

Tools & Technologies

Python spaCy NLTK Pandas Jupyter Notebooks scikit-learn

Role

Lead developer and researcher. Designed the toolkit architecture, implemented core analysis functions, created documentation, and collaborated with literary scholars to validate outputs and refine methodologies.

Outcome

Developed an open-source Python library that enables literary analysis across multiple African languages. The toolkit supports text preprocessing, stylometric analysis, theme extraction, and comparative studies. Used by graduate students and researchers at three universities, contributing to five published papers in digital humanities journals.

Details

Project Overview

The AI-Powered Literary Analysis Toolkit is a specialized Python library designed to bridge the gap between computational linguistics and literary scholarship, with a particular focus on African and multilingual literature.

Background

During my graduate work in comparative literature, I encountered a persistent challenge: existing NLP tools were poorly suited for analyzing literary texts, especially those in low-resource languages or with complex cultural contexts. Most tools were designed for modern, web-scraped English text, not for the rich, nuanced language of poetry and prose from diverse traditions.

This project emerged from that frustration and represents a collaboration between literary scholars and data scientists to create tools that serve humanities research.

Technical Approach

Core Features

Text Preprocessing Pipeline

  • Language detection and tokenization for multiple African languages
  • Literary-aware sentence segmentation
  • Preservation of poetic structure and formatting
  • Support for texts with mixed languages or transliteration

Stylometric Analysis

  • Authorship attribution using statistical methods
  • Vocabulary richness and diversity metrics
  • Sentence complexity and rhythm analysis
  • Comparative stylistic fingerprinting

Theme and Motif Extraction

  • Topic modeling optimized for literary texts
  • Cultural reference identification
  • Metaphor and symbolism detection
  • Character and relationship network analysis

Visualization Tools

  • Interactive plots for exploratory analysis
  • Network graphs for narrative structure
  • Timeline visualizations for historical context
  • Comparative dashboards for cross-text analysis

Implementation Details

The toolkit is built on a foundation of established NLP libraries (spaCy, NLTK) but extends them with custom components:

# Example: Analyzing vocabulary richness across a corpus
from literary_toolkit import Corpus, StyleAnalyzer

corpus = Corpus.from_directory("african_novels/")
analyzer = StyleAnalyzer(corpus)

richness_scores = analyzer.vocabulary_richness(
    metric='type_token_ratio',
    normalize=True
)

analyzer.plot_comparative_richness(richness_scores)

Language Support

The toolkit includes specialized support for:

  • Hausa, Yoruba, Swahili, and other African languages
  • Arabic script and transliteration systems
  • Mixed-language texts common in post-colonial literature
  • Diacritic and tone mark handling

Impact and Usage

Since its release as an open-source project:

  • Academic Adoption: Used in graduate courses at three universities
  • Research Output: Contributed to five published papers in digital humanities
  • Community: Active GitHub repository with contributors from multiple continents
  • Documentation: Comprehensive guides and Jupyter notebook tutorials

Key Insights

  1. Domain Specificity Matters: General-purpose NLP tools often miss literary nuances. Specialized tools trained on literary corpora perform significantly better.

  2. Cultural Context is Critical: Computational analysis must be informed by cultural knowledge. The toolkit includes mechanisms for incorporating cultural annotations and expert knowledge.

  3. Interdisciplinary Collaboration: The most effective tools emerge from ongoing dialogue between technologists and domain experts.

  4. Accessibility: Making tools available as Python libraries (rather than requiring specialized software) dramatically increases adoption in humanities departments.

Future Directions

Ongoing development focuses on:

  • Integration with large language models for deeper semantic analysis
  • Enhanced support for oral literature transcriptions
  • Collaborative annotation tools for building training datasets
  • Mobile-friendly interfaces for field research

Open Source

The complete toolkit is available on GitHub under an MIT license, with extensive documentation and example notebooks. The goal is to empower researchers worldwide to apply computational methods to their literary studies.

This project demonstrates my commitment to building bridges between disciplines and creating tools that serve underrepresented languages and scholarly communities. For collaboration or questions, please reach out.