Sentiment classification

Sentiment classification automatically labels user messages in your LLM traces and generations as positive, neutral, or negative. This helps you understand how users feel during conversations with your AI features without reading every message.

Why use sentiment classification?

• Spot unhappy users – Identify traces where users are frustrated or confused so you can investigate and improve your prompts or agent behavior.
• Monitor quality trends – Track sentiment across models, features, or user segments over time.
• Prioritize improvements – Focus on the conversations that matter most by filtering for negative sentiment.

How it works

Sentiment is computed on-demand when you view traces or generations in the PostHog UI. No batch pipeline or upfront processing is needed.

1. When you open the Traces or Generations tab, PostHog sends a request for each visible row to classify sentiment.
2. A local ML model (cardiffnlp/twitter-roberta-base-sentiment-latest, converted to ONNX) classifies each user message as positive, neutral, or negative.
3. Results are cached for 24 hours so subsequent views load instantly.

No data is sent to third-party services. The model runs entirely within PostHog's infrastructure.

What you'll see

Traces table

Each trace in the traces table shows a sentiment bar indicating the overall sentiment. The bar is color-coded:

• Green – Positive
• Gray – Neutral
• Red – Negative

Hover over the bar to see the sentiment score as a percentage, along with the maximum positive and negative scores across all messages in the trace.

Trace detail view

When you click into a trace, the trace header displays an overall sentiment bar. Each generation within the trace also shows its own sentiment, so you can see how sentiment changes across the conversation.

Generations tab

The generations table also supports sentiment classification. Each generation row shows a sentiment bar based on the user messages in that specific generation.

Per-message breakdown

Sentiment is classified at the individual message level. Each user message within a generation gets its own sentiment score. The overall trace or generation sentiment is the average of all per-message scores, giving you both a high-level view and the ability to drill into specific messages.

Privacy

Sentiment classification runs on PostHog's infrastructure using a local ONNX model. Your data is not sent to any third-party AI provider. The model only processes user messages (the $ai_input property) — it does not process LLM outputs.

Requirements

• LLM events captured – Set up event capture using the installation guide.