Problem
Scenario
You are building a text analysis workflow for a SaaS platform that receives about 200,000 customer feedback comments per month from surveys, support tickets, and app reviews. Product managers want to group comments into themes such as billing, login issues, feature requests, and performance complaints, and they also want a representation that supports similarity search for related comments. The text is short and noisy, with misspellings, duplicated boilerplate, emojis, and a mix of one-line comments and multi-sentence descriptions. You have a partially labeled historical dataset and need an approach that is practical to train, explain, and iterate on.
Question
How would you use TF-IDF and/or word embeddings to build this text analysis system, and how would you decide which representation is better for classification, clustering, and similarity-based exploration in production?
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