Sentiment Analysis

The canonical NLP task. Most of what you need to know about classical text classification shows up here.

Build 1.3 hr Python

Prerequisites: Phase 5 · 02 (BoW + TF-IDF), Phase 2 · 14 (Naive Bayes)

#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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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.