Ch 10: Natural Language Processing Basics - Introduction¶
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Chapter 10: NLP Basics — Notebook 01 (Fundamentals)¶
This notebook introduces the core building blocks of NLP: tokenization, text preprocessing, text representation (Bag of Words, TF-IDF, embeddings), and your first sentiment analysis pipeline.
What you'll learn¶
| Topic | Section |
|---|---|
| Text preprocessing (tokenization, stemming, lemmatization, stopwords) | §2 |
| Bag of Words, TF-IDF, one-hot encoding | §3 |
| Reusable preprocessing pipeline | §4 |
| Word embeddings (GloVe, similarity, analogies) | §5 |
| First sentiment analysis with TF-IDF + logistic regression | §6 |
Time estimate: 2.5–3 hours
Key concepts¶
- Tokenization — Split text into words or sentences (e.g. NLTK
word_tokenize,sent_tokenize). - Stemming vs lemmatization — Reduce words to base form; lemmatization uses dictionary form.
- TF-IDF — Term frequency–inverse document frequency to highlight discriminative terms.
- Word embeddings — Dense vectors for words (e.g. GloVe) so similar words are close in space.
- Sentiment analysis — Preprocess text → TF-IDF features → train a classifier (e.g. logistic regression) → evaluate.
Run the full notebook in the chapter folder for code and outputs.
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