11.3.1 Text Classification Roadmap: Text In, Label Out

Text classification takes one piece of text and predicts one label, such as sentiment, topic, intent, or risk type.

See the Classification Pipeline First

Always build a baseline before a complex model. Most classification problems fail because labels are vague or examples are skewed.

Run a Keyword Baseline

texts = ["great course and clear examples", "confusing setup error"]
positive_words = {"great", "clear", "good", "useful"}

for text in texts:
    score = sum(word in positive_words for word in text.split())
    label = "positive" if score > 0 else "needs_review"
    print(label, "-", text)

Expected output:

positive - great course and clear examples
needs_review - confusing setup error

Simple baselines are not the final model, but they expose label rules and failure cases quickly.

Learn in This Order

Step	Read	Practice Output
1	Traditional methods	Build TF-IDF or keyword baseline
2	Deep learning methods	Compare embeddings, pooling, CNN/RNN/Transformer features
3	Project practice	Track split, metrics, label ambiguity, and error samples

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Label Schema

label definitions and boundary examples

Dataset Split

fixed train/test examples or evaluation set

Prediction

predicted label, expected label, and confidence or score

Failure Check

class imbalance, label overlap, leakage, or confusing wording

Expected Output

metrics plus error samples grouped by failure reason

Pass Check

You pass this chapter when you can train or simulate a classifier, report accuracy/F1, and explain at least one ambiguous label case.

Check reasoning and explanation

1. A passing answer starts from the text unit and output type: token, span, sentence label, sequence, embedding, or generated text.
2. The evidence should include a small dataset example, model or pipeline choice, metric, and at least one inspected error case.
3. A good self-check distinguishes preprocessing issues from model issues, such as tokenization mistakes, label ambiguity, data imbalance, or hallucinated generation.