Text Classification Pipelines
A text classification pipeline turns raw documents into category predictions through a series of stages. Spam detection, topic labeling, and intent routing all follow the same shape.
The stages usually run in order:
- Preprocessing, which lowercases text, strips noise, and may remove very common stop words
- Tokenization, which splits text into words or subword units
- Feature extraction, which converts tokens into vectors via bag of words, TF IDF, or embeddings
- Modeling, where a classifier maps the vector to a label
- Evaluation, which measures quality on held out data
Keeping these as separate, reusable steps matters. A crucial rule is to fit on training data only. If you compute vocabulary or TF IDF statistics using the test set, information leaks and your reported accuracy is inflated. The pipeline should learn its transforms from training, then apply them unchanged to new data.
For imbalanced problems like spam, plain accuracy misleads, since predicting the majority class can look good while missing the minority. Precision and recall give an honest picture.
Wrapping the whole sequence in one pipeline object also prevents subtle bugs, because the exact same preprocessing runs at training and at serving time.
Key idea
A text classification pipeline chains preprocessing, feature extraction, and a classifier, fitting transforms on training data only to avoid leakage.