What is topic clustering?
Topic clustering groups customer conversations by what they are about, such as billing, login problems or a specific feature, so sentiment and volume can be traced to a cause instead of reported as one average.
How topic clustering works
Each conversation is turned into a numeric representation of its words or meaning, and conversations that sit close together are grouped. Each group is then labelled by its most characteristic terms. Unlike manual ticket categorization, it finds topics nobody thought to create a tag for.
A topic clustering example
A month of 4,000 tickets clusters into 12 topics. Most are routine, but one small cluster of 180 tickets mentions “invoice”, “VAT” and “wrong amount” with sharply negative sentiment. One average score for the month would hide it; the cluster points straight at a billing bug.
Topic modeling vs clustering
Topic modeling, such as LDA, assumes each document can mix several topics and estimates the proportions. Clustering usually places each document in one group. For support tickets, which tend to be about one thing, clustering is often simpler and easier to act on.
From clusters to fixes
Customer Support Sentiment Analyzer clusters complaints by topic and ranks them by volume and tone, which turns a pile of tickets into a short list of fixes.
Last reviewed 2026-11-01.