Am I the only one who finds the sentiment labels too simplistic?

You're definitely not the only one, and you've hit on the core limitation of these off-the-shelf sentiment models. They're built for generic accuracy benchmarks, not operational triage. The conflation of "slow" and "down" under a single "negative" label is a perfect example of why raw sentiment is a poor direct alert source.

We faced the same issue and built a pre-processing layer. Before feeding text to Cartesia, we run it through a simple rule-based classifier that looks for severity keywords and phrases, then enriches the payload with a `severity_hint` label. Cartesia's sentiment becomes just one more dimension. For instance, a message containing "outage" or "cannot process payments" gets a `severity_hint="critical"` before it's even evaluated for sentiment. This allows alerting on `severity_hint="critical"` AND `sentiment="negative"`.

The docs are vague because, in my experience, Cartesia's API doesn't expose model sensitivity tuning. Your idea of a keyword scorer is the right path. The real heavy lifting is in building your own taxonomy of what "negative" means for your business context. Have you considered using Cartesia's output as a feature input to a separate, internal prioritization model you control?

Absolutely not alone in this frustration! The simplistic labels completely break down when you try to use sentiment for operational urgency. Your "slow" vs "down" example is spot on - they demand completely different responses.

We actually tried that keyword scoring idea you mentioned. It's a decent stopgap, but we found it creates a maintenance nightmare. You have to constantly update the keyword lists as new issues and slang pop up in chats. It's like playing whack-a-mole.

I'm curious - have you looked at the actual confidence scores Cartesia provides for each label, not just the final tag? Sometimes a message tagged as "negative" with a 51% confidence score is worlds different from one with a 95% score. We've had some success using that as a crude severity weight, though it's still not perfect for catching those critical phrases.

Confidence scores as a severity proxy is clever, but you're just adding another heuristic layer to a broken foundation. It's still guessing.

If a message like "system is down" gets a 51% negative score because the phrasing is neutral, your weight-based alerting misses it entirely. You're now tuning two unreliable systems instead of one.

I've run into the exact same limitation with the three-label output. You're correct that for operational triage, the coarse classification is a significant blocker. Your idea about a pre-processing keyword scorer is the right direction, but I'd suggest a different architectural approach based on what we had to implement.

Instead of piping text *through* a scorer before Cartesia, we pipe the *results* from Cartesia through a secondary, domain-specific classifier that runs on our side. This secondary model uses the raw text *and* Cartesia's sentiment label (and its confidence score) as input features. We trained it on a few thousand hand-labeled support messages from our own history, focusing on the severity dimension you mentioned - "slow" vs "down" vs "billing issue".

The key was to treat Cartesia as a generic sentiment feature extractor, not a decision engine. Its API gives you the label and score; you then feed that into your own logic layer that understands your business context. This separates the general sentiment analysis from the specific operational severity you need.

This does require maintaining your own model, but it's more sustainable than a keyword list. The training data stabilizes after a while, and you retrain far less often than you'd update keyword rules.

For your alert rule, you'd then alert on your custom `operational_severity` label (e.g., "critical", "degraded", "neutral") instead of the raw `negative` count. It moves the heavy lifting from your team reading 50 chats to the model, where it should be.

You're right that the three-label output is a severe limitation for operational triage. Your example of the alert firing and then requiring manual sifting through dozens of chats is exactly the problem.

Your keyword scorer idea is a logical first step. We implemented a similar pre-processor, but found it brittle. The maintenance burden of curating keyword lists was high, and it missed nuanced but critical complaints.

The architectural shift that worked for us was to treat Cartesia's sentiment as just one input feature, not the final classification. We built a secondary classifier on our infrastructure, trained on our own historical support messages labeled for operational severity (like "performance degradation" vs "outage"). This model consumes the raw text *and* the Cartesia label/confidence score. It outputs a severity tag we can alert on directly. This moves the heavy lifting from your team post-alert to the pipeline itself.

You might start by exporting a few hundred past support messages, manually tagging them with your internal severity levels, and training a simple model (like a scikit-learn text classifier) as a proof of concept. This approach acknowledges that off-the-shelf sentiment is too generic for your domain.

Welcome to vendor analytics. Slick dashboards are cheap. Actionable intelligence is expensive.

You haven't missed a configuration. They just don't sell one. The "missed opportunity" is the point. Their job is to sell you a metric, not a solution.

Your keyword scorer idea is a band-aid you'll end up maintaining forever. And wait until you see what their "Enterprise" plan charges for custom model tuning. That's where the real feature is hiding.