1. Introduction & Theoretical Framework

This study investigates computational methods for analyzing unstructured text data in scholarly research.

2. Methodology & Data Ingestion

  • Data Harvesting: Unstructured texts harvested through open APIs and web scraping pipelines.
  • Preprocessing: Normalization, tokenization, lemmatization, and stop-word filtering.
  • Model Training: Topic modeling (LDA) and sentiment classification using supervised and unsupervised machine learning algorithms.

3. Conclusions & Reproducibility

Computational workflows enable researchers to categorize thousands of textual documents rapidly, offering transparent and reproducible alternatives to traditional manual content analysis.