Your question is Analyze Healthcare Customer Feedback Themes. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are supporting a healthcare services organization that receives customer feedback through post-visit surveys, call-center notes, email complaints, and free-text NPS comments. The business wants a repeatable NLP workflow that can identify major themes such as scheduling, billing, site experience, and staff interactions, while also measuring sentiment at the comment level and trend level. You have roughly 300,000 historical comments collected over 18 months, comments range from 5 words to several paragraphs, and the text contains misspellings, abbreviations, duplicated boilerplate, and occasional PHI. Only a small subset has manually reviewed sentiment labels, and stakeholders want outputs that analysts can explain and validate.
How would you design and implement an NLP solution to preprocess this feedback, identify themes, and assign sentiment in a way that is accurate, interpretable, and useful for downstream reporting and operational decision-making?