From TextBlob to LLM Agents: Sentiment Model Selection for B2B Technical Support with CSAT Ground Truth (2026.acl-industry)
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| Challenge: | a dedicated single-task LLM agent reduces neutral bias from 69% to 22%, improving MCC from -0.018 to 0.347 . only 4.88% of tickets receive negative satisfaction ratings . |
| Approach: | They evaluate sentiment models for customer satisfaction prediction in B2B technical support . they use a complete population of CSAT-rated tickets from 100+ organizations . |
| Outcome: | The proposed model performs better than the most expensive model, with a lower neutrality and lower recall than the budget model. |
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