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AI Search Sentiment page with trend, positive and negative split, and All tab
Use this page to track brand perception quality in AI answers.

Important: One negative mention is noise. Repeated negative themes across prompts are the real risk.

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Questions this page should answer

  1. Is sentiment improving or getting riskier?
  2. Which themes are driving positive or negative perception?
  3. What messaging updates should we ship first?

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Before you analyze

  • Match date range with the rest of AI Search reporting.
  • Read trend direction before row-level details.
  • Separate positive and negative themes before acting.

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What this page gives you

  • Positive sentiment trend line.
  • Positive vs negative share summary.
  • Theme-level evidence table with sources.
  • Three subtabs: All, Positive, and Negative.

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How to read the top sentiment section

  • Trend line shows narrative direction over time.
  • Right-side split shows current narrative balance.
  • Theme rows show what is shaping that balance.

Key signal: If visibility stays stable but negative share rises, conversion risk is usually increasing before traffic metrics react.

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How these metrics are calculated (simple)

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Positive share

Positive share = (Positive-labeled responses / total sentiment-labeled responses) x 100

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Negative share

Negative share = (Negative-labeled responses / total sentiment-labeled responses) x 100

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Theme rows

Theme rows are recurring sentiment statements grouped into actionable theme clusters.

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All tab

Start in All for a full narrative baseline.
AI Search Sentiment with All tab selected
Use it to identify:
  • Mixed themes requiring clearer positioning.
  • Repeated concerns that hurt trust.
  • Positive messages worth scaling.

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Positive tab

Use Positive to preserve and scale what already works.
AI Search Sentiment with Positive tab selected
Focus on:
  • Positive themes that repeat across sources.
  • Signals you can reuse in landing pages and prompts.
  • Source types that produce high-trust mentions.

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Negative tab

Use Negative to reduce risk quickly.
AI Search Sentiment with Negative tab selected
Focus on:
  • Recurring negative themes.
  • Negative themes tied to decision queries.
  • Source patterns that repeatedly create risk.

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Quick weekly checklist

  1. Check positive vs negative balance.
  2. Flag top recurring negative theme.
  3. Protect one high-impact positive theme.
  4. Assign one message/proof fix per sprint.

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How to use filters

  • Start with All before narrowing.
  • Compare Positive and Negative separately.
  • Keep window consistent to avoid false trend shifts.

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What to fix first

Pattern in Sentiment dataWhat it usually meansRecommended action
Negative share rising on trust topicsCredibility concernAdd stronger proof and clearer claims
Positive stable, conversions weakeningNarrative not supporting decisionsImprove decision-stage messaging
One negative theme repeatsStructural positioning gapUpdate core narrative and FAQ/support content
Positive themes come from few sourcesFragile narrative baseExpand high-quality source distribution

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Team routine

  1. Weekly: review drift and assign fixes.
  2. Bi-weekly: verify whether negative themes were reduced.
  3. Monthly: align messaging roadmap with sentiment trends.

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Keep in mind

  • Sentiment is directional, not absolute truth.
  • Short windows can be noisy.
  • Repeated themes matter more than isolated mentions.

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Where to go next