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Web Mining and Applied NLP Project Report

Web Mining and Applied NLP | Project 02 | Example Report

Overview

This project introduces text preprocessing as a foundation for natural language processing.

The objective is to demonstrate how raw text can be tokenized, normalized, cleaned, and prepared for systematic analysis.

The project compares the results of different preprocessing operations to show how cleaning decisions affect the representation of text.

Tools and Methods

The project uses:

  • Python for text processing and analysis.
  • Local text files for input data.
  • Regular expressions for punctuation removal.
  • Tokenization for separating text into words.
  • Lowercasing for text normalization.
  • Stopword filtering for reducing common vocabulary.
  • Polars for calculating token frequencies.
  • Matplotlib for visualizing preprocessing results.
  • Logging for execution records.
  • Ruff, ty, and pytest for code verification.
  • Zensical and GitHub Pages for published documentation.

The example script reads text records, combines them into a corpus, and applies a sequence of preprocessing operations.

The analysis compares raw and cleaned token counts and identifies frequently occurring words.

Verification

A successful execution displays:

Pipeline executed successfully!

The script records execution details in a project log.

The output includes token counts, frequency distributions, and preprocessing comparison charts.

Automated tests provide additional verification of the project's expected behavior.

Interpretation

Text preprocessing transforms raw language into a representation suitable for analysis.

Lowercasing, punctuation removal, and stopword filtering change the resulting token distribution.

These operations can reduce unwanted noise, but they may also remove meaningful information.

Understanding these tradeoffs is important when preparing text for traditional NLP and modern language-model applications.

Documentation Index

Project Resources