Phase 0: NLP Foundations · ~75 minutes · Python
Sentiment Analysis
The canonical NLP task. Most of what you need to know about classical text classification shows up here.
What you will learn
- Implement sentiment analysis from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
- Type: Build
- Languages: Python
- Prerequisites: Bag of Words, TF-IDF, and Text Representation, Naive Bayes (Core ML: Algorithms from Scratch), Naive Bayes
- Time: ~75 minutes
The Problem
"The food was not great." Positive or negative?
Sentiment sounds simple. A reviewer said they liked or did not like something. Label the sentence. The reason it became the canonical NLP task is that every easy-looking case hides a hard one. Negation flips meaning. Sarcasm inverts it. "Not bad at all" is positive despite two negative-coded words. Emojis carry more signal than surrounding text. Domain vocabulary matters (tight in music review versus tight in fashion review).
Sentiment is a working lab for classical NLP. If you understand why every naive baseline has a specific failure mode, you understand why every richer model was invented. This lesson builds a Naive Bayes baseline from scratch, adds logistic regression, and names the traps that make production sentiment a compliance-grade problem.
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You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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