AI Sentiment Score
What is AI Sentiment Score?
AI sentiment score measures how favorably AI assistants describe your brand in their responses. It goes beyond whether you're mentioned to capture whether you're recommended enthusiastically, listed neutrally, or mentioned with caveats and criticisms.
Why sentiment matters more than mentions
Being mentioned by AI isn't always good. A brand mentioned as "an option but not recommended for serious use" is worse off than not being mentioned at all. Sentiment separates visibility from favorability.
Consider two scenarios:
- Brand A: mentioned in 80% of prompts, but often with caveats ("limited features," "better alternatives exist")
- Brand B: mentioned in 40% of prompts, but consistently recommended ("top choice," "excellent for...")
Brand B is in a stronger position despite lower prompt coverage. Sentiment determines whether AI mentions help or hurt your sales pipeline.
Sentiment analysis categorizes each mention into:
- Positive: recommended, praised, cited as a leader
- Neutral: listed as an option without strong opinion
- Negative: mentioned with criticisms, caveats, or unfavorable comparisons
Tracking sentiment across models
AI sentiment can vary dramatically across models. A model trained on positive reviews might describe you favorably, while another that ingested an old critical blog post might be less kind.
| Signal | Example | Impact |
|---|---|---|
| Strong recommendation | "X is the top choice for..." | High positive |
| Qualified endorsement | "X is a good option if you need..." | Moderate positive |
| Neutral listing | "Options include X, Y, and Z" | Neutral |
| Qualified criticism | "X works but lacks..." | Moderate negative |
| Explicit warning | "X is not recommended for..." | High negative |
Prompt Metrics scores sentiment across all major models and tracks shifts over time. A sudden sentiment change on a specific model often signals a training data update.
Improving your sentiment profile
Sentiment improvement is a long game, but it follows a clear playbook:
- Figure out which specific claims or descriptions are dragging sentiment down
- If AI says you lack a feature you've since shipped, update your content everywhere: product pages, documentation, third-party listings
- Publish case studies, customer success stories, and benchmark data on authoritative domains
- Earn genuine reviews on G2, Capterra, and TrustRadius. They feed directly into AI sentiment
- Publish fair, data-backed comparisons that position your strengths accurately
Sentiment shifts gradually as models incorporate new training data. Track weekly with Prompt Metrics to catch improvements (or regressions) early.
Frequently Asked Questions
AI responses mentioning your brand are analyzed for tonal signals: positive language ("leading," "top-rated"), neutral language ("an option," "also available"), or negative language ("limited," "lacks"). These signals are scored and aggregated across models and prompts. Prompt Metrics automates this analysis.
Aim for predominantly positive sentiment (70%+ positive mentions). Some neutral mentions are expected. Negative mentions above 10% are a red flag that warrants investigation. The trend matters more than the absolute number.
Yes, frequently. AI sentiment reflects the training data the model learned from, which may include outdated reviews or old press coverage. Your actual product may have improved significantly, but AI hasn't caught up. This is a core challenge of AI reputation management.
Influence the underlying signals: publish positive case studies on high-authority domains, earn reviews on G2 and Capterra, create content that addresses common criticisms with data, and correct outdated information across the web.