Industry VOC Deep Semantic Clustering Analysis

What it addresses

When product improvement, satisfaction analysis, or opportunity discovery depends on user comments, replies, and follow-ups, raw feedback is often scattered, colloquial, and mixed with low-value fields. Reading a spreadsheet directly can hide repeated customer issues beneath noise. This skill turns a batch of VOC material into a stable topic structure that can be reviewed and used for decisions.

How it works

It starts with two checks: report the read coverage of the source material and exclude invalid items such as empty comments, template auto-replies, metadata without analytical value, ads, or spam. It then performs internal reasoning to identify recurring concepts, tag comments by industry dimensions, and merge semantically similar concepts into higher-level topics. The output is a fixed five-column Markdown table with No., Topic, Description, Supporting comments, and Voice labels, usually containing 8–12 topics. Each topic includes 2–4 original user quotes and short labels such as too salty, hard packaging, or slow delivery.

Boundaries

It fits Excel, CSV, JSON, TXT, PDF, or pasted comments when the target industry, product, or keyword is known. If coverage is below 60%, it recommends batching or sampling. It is a topic-clustering workflow, not a replacement for quantitative statistics, emotion models, or full BI analysis.