Scenario

You are working with a dataset made up of raw, unstructured text such as emails, support notes, PDFs converted to text, and free-form comments. Before you can model anything useful, you need to turn that text into structured signals that can support downstream tasks like labeling, search, routing, or analytics. A strong approach usually combines preprocessing, feature extraction, and task-specific modeling depending on what the business needs from the data.

Question

How do you work with natural language processing on unstructured datasets?