Text Cleaning and Normalization Toolkit for NLP Research

Added Jun 2025 3 design docs

Every NLP project lives or dies on preprocessing, and real-world text is messy: inconsistent casing, punctuation debris, emojis, hashtags, and inflected word forms all degrade downstream models when handled carelessly. Researchers waste enormous time rewriting the same cleanup scripts, and subtle inconsistencies between projects make results hard to reproduce. The intern builds a Python command-line toolkit that standardizes this stage. Commands process raw text files through a configurable sequence of operations: cleaning and normalization, tokenization, stopword removal, and both stemming and lemmatization, implemented with NLTK and spaCy so users can compare the libraries' behavior on the same input. Special handling for emojis and hashtags lets users strip, keep, or translate social-media artifacts depending on the target task. The tool writes cleaned, tokenized output ready for vectorization or model training, and its composable design means each stage can run independently or as a full pipeline. The project teaches the intern why preprocessing decisions are modeling decisions, how stemming and lemmatization differ in accuracy and cost, and how to design a Unix-style tool with clear inputs and outputs. They finish with deep practical knowledge of the two dominant Python NLP libraries and a utility other researchers can adopt.

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