You are building an NLP pipeline that needs to assign text into predefined categories, such as routing support tickets, tagging documents, or labeling incoming messages. You need a practical approach that covers data preparation, feature extraction, model training, and evaluation. The goal is to produce a system that works well on real text, not just a toy dataset.
What are the key steps in building a text classification system?
Text preprocessing and tokenizationFeature extraction with TF-IDF or transformer embeddingsSupervised text classification workflowEvaluation with class-aware metrics such as F1