Sentiment Analysis
The canonical NLP task. Most of what you need to know about classical text classification shows up here. "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. Classical sentiment is a two-step recipe. Represent. Turn the text into a feature vector. BoW, TF-IDF, or n-grams. Classify. Fit a linear model (Naive Bayes, logistic regression, SVM) on labeled examples. Naive Bayes is the dumbest model that works. Assume every feature is independent given the label. Estimate P(word | positive) and P(word | negative) from counts. At inference, multiply the probabilities. The "naive" independence assumption is laughably wrong and yet the results are shockingly…
Sentiment Analysis: The canonical NLP task. Most of what you need to know about classical text classification shows up here. "The food was not great."…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
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