Cleaning and tagging 8,500 survey rows is exactly the kind of bulk data work AI handles well.

The same pipeline—normalize text, deduplicate, classify sentiment, assign topic bucket—applies to every row. The structure is identical across all 8,500 records, making this highly automatable.

Output columns and topic buckets are explicitly named, which is good. However, edge cases exist: responses that span multiple topics, ambiguous sentiment, and which acronyms to expand are judgment calls that need a defined ruleset or will produce inconsistent results.

The input is a Google Sheet (easily exported to CSV), and the output format is a clean CSV—no external APIs or live data required. A code agent or LLM pipeline can process this entirely offline with standard tools.

Misclassified sentiment or a wrong topic tag is easily caught in a spot-check and corrected. The output is a CSV, not a published decision or irreversible action, so errors are cheap to fix.

Sentiment and topic classification at this granularity are well within current LLM capability. The only genuine human judgment needed is defining the acronym expansion list and reviewing a sample of multi-topic or ambiguous responses.