Our current rule-based sentiment analysis is failing to catch subtle sarcasm or escalating frustration in chat logs. I'm looking into moving toward a transformer-based model (like BERT or RoBERTa) to better understand the "emotional intent" of our users. Has anyone successfully implemented this in a live environment, and how did it affect your CSAT scores or human hand-off rates?
3 answers
Switching to a RoBERTa-based model was a game-changer for our support team. The old system only looked for keywords like "angry" or "bad," but the new model understands context. For instance, it can now distinguish between "This is great!" (happy) and "Oh, this is just great..." (sarcastic). We saw our Customer Satisfaction (CSAT) scores jump by 18% because the bot now proactively offers a human agent the moment it detects a "frustration threshold" being crossed. Just a tip: make sure you fine-tune the model on your actual chat transcripts rather than just using a generic pre-trained dataset.
What specific threshold are you using to trigger the human escalation, and do you let the user know the bot is sensing their mood?
We found that hybrid models work best—combine the transformer model with a simple keyword list for immediate "red-flag" words like legal threats or profanity.
Exactly, Barbara. Combining Deep Learning with "hard rules" for critical situations ensures you have a safety net while the AI handles the nuances.
Christopher, we set our threshold at a 0.8 probability of "Negative Sentiment" sustained over two exchanges. We don't explicitly tell the user "I see you're mad," as that can be annoying. Instead, the bot says something like, "I want to make sure we get this right for you; let me connect you with a specialist who can dive deeper into this." It feels much more natural and empathetic to the customer.