Text Classification, Sentiment, Topics and Clustering

Why this module belongs in the course

A text-mining score is meaningful only when the labels, sampling and error categories match the decision the system must support.

Before you begin

The concepts and project evidence from Module 11. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1Build a Leakage-Safe Text Classification Baseline75 min · Intermediate
2. Lesson 2Sentiment Analysis with Honest Error Analysis70 min · Intermediate
3. Lesson 3Topic Modelling and Text Clustering That You Can Inspect80 min · Intermediate
4. Lesson 4Project: Build a Reviewed Support Ticket Triage System110 min · Intermediate

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
• https://scikit-learn.org/stable/modules/model_evaluation.html
• https://scikit-learn.org/stable/common_pitfalls.html
• https://huggingface.co/docs/hub/datasets-cards
• https://scikit-learn.org/stable/modules/decomposition.html
• https://scikit-learn.org/stable/modules/clustering.html