DocSim: Document Similarity Analysis Tool

A Python-based implementation designed to calculate semantic similarity between textual documents using Vector Space Modeling and Cosine Similarity.

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Project Structure

The repository is organized to demonstrate both the initial logic and the improved modular version:

• main.py: The primary entry point for executing the analysis.
• preprocess.py: Dedicated module for text normalization and tokenization.
• similarity.py: Core logic for vector generation and cosine calculations.
• file_loader.py: Utility module for handling document input operations.
• utils.py: General-purpose helper functions.
• old_version.py: The initial single-script implementation (Version 1.0).

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Technical Stack

• Language: Python 3.x
• Methodology: Vector Space Modeling (VSM)
• Metric: Cosine Similarity
• Domain: Natural Language Processing (NLP)

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Key Features

• Modular Architecture: Code is decoupled into specific modules for better readability and maintenance.
• Preprocessing Pipeline: Robust handling of raw text to ensure accurate similarity scoring.
• Clean Code: Follows structured programming principles to make the logic easy to follow.

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How to Use

1. Clone the repository to your local environment.
2. Run the program using:

   python main.py