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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. Open the Sentiment page from the sidebar.

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How it’s determined

Every response that mentions your brand is scored, 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.
The headline numbers are a confidence-weighted balance of positive versus negative phrases — a phrase the model is sure about counts for more than a borderline one.
Sentiment is only assessed on responses where your brand is actually mentioned — non-mentions never dilute the picture.

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What the page shows

1

Sentiment over time

A trend line of your positive share across the selected range. Click any point to read the individual phrases behind that period.
2

Overall sentiment

An at-a-glance gauge with a plain-language label (Very positive → Very negative), the positive/negative split, and the change versus the previous period.
3

Topic sentiment

One card per tracked topic — how you’re spoken about on each, how you rank against competitors on it, and the most-cited positive and negative phrases.
4

Sentiment themes

The recurring claims and objections AI keeps making about you, cutting across topics rather than restating them. Each theme opens into the responses behind it, and an emerging theme can be promoted to a tracked topic.
5

Attribute radar

How positively AI talks about you on each of your brand attributes — a direct read on whether perception matches the qualities you want to be known for. Add attributes on your brand profile to light this up.
Themes and attribute scores are computed per brand, market, and date range. When you pick a new period, a short “Analysing sentiment for this period” strip appears while it’s prepared.

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How to read it

Look at the distribution, not just the headline. 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. Use Sentiment themes to find the why fast: they surface the recurring objection (price, support, a missing feature, an outdated claim) instead of making you read every answer to spot the pattern.

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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 — a natural brief for the Content Agent.
Sort conversations by negative sentiment on Must-Win prompts first — that’s where bad framing costs you the most revenue.