What's the most cost-effective way to classify tickets and feedback using a topic modeling software?

The most cost-effective approach is usually:

Use unsupervised topic modeling + lightweight rules/human review, not a fully supervised classifier

If you’re classifying tickets and feedback at low cost, start with a topic model to discover themes, then map those themes to your business categories.

Best low-cost setup

1. Clean and normalize text

   • Remove signatures, boilerplate, stop words
   • Normalize spelling, emojis, URLs, product names if needed

2. Run topic modeling

   • Good options:
     • BERTopic: often best for short tickets/feedback; strong quality, easy to use
     • LDA: cheaper/simple, but often weaker on short texts
     • NMF: good middle ground, fast and interpretable
   • For customer tickets/feedback, BERTopic is usually the most practical choice.

3. Label topics once

   • Review the top words and sample documents for each topic
   • Assign each topic to a business label like:
     • Billing
     • Login issues
     • Feature request
     • Bug report
     • Shipping delay

4. Use topic assignments as classification

   • Each ticket gets the dominant topic
   • If needed, allow multi-label assignment when a ticket fits multiple topics

5. Add a small rules layer

   • Keyword rules for high-confidence cases
   • This reduces manual work and improves precision

6. Periodic retraining

   • Re-run topic modeling on new data monthly or quarterly
   • Merge, split, or relabel topics as themes change

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Why this is most cost-effective

• No large labeled dataset needed
• Low implementation cost
• Fast to start
• Easy to adapt when ticket types change
• Human effort is focused only on labeling topics, not every ticket

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When this is better than supervised classification

Use topic modeling if:

• You have little or no labeled data
• Categories change often
• You want to discover unknown issues
• You need a quick, cheap first pass

Use supervised classification if:

• You already have thousands of labeled tickets
• You need very high accuracy
• Categories are stable

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Practical recommendation

If you want the cheapest useful solution:

• BERTopic + human labeling + simple keyword rules
• Deploy it as a batch process first
• Only move to a supervised model later if accuracy or scale demands it

If you want, I can also recommend the best topic modeling tool by budget or give you a step-by-step implementation workflow.