You are working with a collection of unstructured text such as customer complaints, field engineer notes, and internal service logs. The goal is to turn raw text into usable insights for downstream analysis and decision-making. You may need to clean noisy language, represent documents numerically, identify entities and themes, and train models that can organize or label the content.
How would you handle unstructured text data to extract meaningful insights using machine learning models?
Text preprocessing and tokenization choicesSparse features such as TF-IDFTopic modeling for unlabeled theme discoveryNamed entity extraction from domain textSupervised text classification for structured outputs