NLP · Text Mining
Shakespeare Text Analysis with NLTK
Natural Language Processing exploration of Shakespeare’s works using NLTK, focusing on tokenization, frequency analysis, and stylistic comparisons across plays.
Overview
This project uses NLTK to process a corpus of Shakespeare’s plays and examine how language, word usage, and patterns differ between works. It demonstrates how to move from raw text to structured insights using standard NLP techniques.
The analysis includes tokenization, stopword removal, n-gram extraction, and basic sentiment-style explorations to understand tone and style at a high level.
Data & Methods
Text Corpus
- Collection of Shakespeare plays loaded as text files / corpus.
- Subsets for comparing tragedies vs comedies.
NLP Processing
- Tokenization of raw text into words using NLTK.
- Lowercasing, stopword removal, and punctuation stripping.
- Part-of-speech (POS) tagging and bigram extraction.
Explorations
- Word frequency analysis to find the most common tokens.
- Bigram frequencies to capture common word pairs and phrases.
- Simple comparisons of vocabulary usage between genres.
Tech Stack
Python, NLTK, pandas, matplotlib, Jupyter
Key Charts
Swap in any plots you generated (frequency distributions, bigram charts, etc.).
Challenges & Learnings
- Dealing with older English spelling and vocabulary when cleaning the text.
- Understanding how stopword choices affect frequency-based insights.
- Getting comfortable with NLTK’s tokenization and POS tagging APIs.
- Seeing how small NLP building blocks can lead to interesting literary insights.