Document Analysis with TF-IDF

Project Overview

This project explores how computational linguistics can be applied to extract meaningful patterns from document collections. I implemented a tf-idf (term frequency-inverse document frequency) vector model that generates relevance ratings for documents, with capabilities to process text from various formats including PDF files.

Key Aspects

• Developed a complete information retrieval pipeline for document analysis
• Implemented preprocessing steps for cleaning and normalizing text data
• Created a vector model implementation of tf-idf for document relevance scoring
• Built functionality to handle documents in different formats, particularly PDF files

Interdisciplinary Applications

This work sits at the intersection of computer science, linguistics, and information science. The system can be applied to various domains including legal document analysis, literature research, and content recommendation systems. It demonstrates how computational methods can augment human analysis of large text collections.

Methodological Considerations

The project explores the balance between purely statistical approaches and more nuanced linguistic analysis. While tf-idf provides a powerful statistical foundation, the implementation considers linguistic aspects such as stemming, stop words, and contextual relevance.

Technologies Used

• Python for core implementation
• Natural language processing libraries
• PDF parsing capabilities
• Vector space modeling for document representation