Text classification

Text classification gives a whole text a label, like positive or negative, spam or not, or a topic.

• Start simple: TF-IDF features with logistic regression.
• Fine-tune a small transformer when you need more accuracy.
• Multi-label: a text can have several labels at once.

pos, neg = {"good", "love"}, {"bad", "awful"}
words = "i love this phone but the battery is bad".split()
score = sum(w in pos for w in words) - sum(w in neg for w in words)
print("positive" if score > 0 else "negative" if score < 0 else "neutral")

neutral

Remember: Always compare against a simple baseline.

Common mistake: Training on balanced data and deploying on data where 95% is one class.