Skip to main content
Sentiment captures the tone AI assistants use about your brand. Being mentioned a lot is only good if the framing helps you — sentiment tells you whether the answer is selling for you or against you.

​
How it’s determined

Every response that mentions your brand is classified as positive, neutral, or negative, and the dashboard shows the split across those three. The analysis works at two levels:
  1. Response level. AI analysis evaluates the overall tone toward your brand in each response.
  2. Sentence level. Individual brand-mentioning sentences are analyzed with a vocabulary-based system — positive words (“excellent”, “leading”), negative words (“terrible”, “unreliable”), negation handling (“not good” → negative), and intensifiers (“very reliable” → strongly positive). Works in English, Danish, Swedish, Norwegian, and German.
Sentiment is only assessed on responses where your brand is actually mentioned — non-mentions never dilute the picture.

​
How to read it

Look at the distribution. A half-positive/half-negative split is a very different story from uniformly neutral coverage — the first means AI is actively arguing about you; the second means it has nothing strong to say. Drill into negative mentions in Conversations to find the recurring reasons: price, support, a missing feature, an outdated claim.

​
How to improve it

  • Correct outdated or inaccurate claims at their source — your own pages and the review sites AI cites.
  • Strengthen the third-party sources AI leans on for your category. See Authority & Outreach.
  • Address recurring negative themes in your own content, so the model has a positive, current alternative to cite.
Sort conversations by negative sentiment on Must-Win prompts first — that’s where bad framing costs you the most revenue.