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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. Screenshots use illustrative local themes and evidence.

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

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?

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.

What this page gives you

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

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.

How these metrics are calculated (simple)

Positive and negative share

These percentages use the impact of assessed evidence. Each positive or negative observation has an impact weight: 1 for a minor or indirect signal, 2 for a material supported claim, or 3 for major direct evidence.
Neutral observations and observations without an impact assessment do not enter this denominator. For example, positive evidence with total weight 6 and negative evidence with total weight 3 produces 66.7% positive and 33.3% negative share, regardless of how many neutral observations exist. The trend compares positive share with the preceding period in percentage points. When no assessed positive or negative evidence exists, the page shows Insufficient sentiment evidence; do not read it as a negative verdict.

Theme rows and sentiment score

Recurring statements are grouped into themes. Expand a theme to inspect the individual observations and open its evidence before deciding what to change. Occurrences counts observations; it is separate from impact. A theme’s sentiment score averages signed assessed impact on a scale from −100 to 100. A positive weight of 3 contributes +100, a negative weight of 3 contributes −100, and a weight of 1 contributes about ±33.3. Neutral-only themes score 0. Missing assessments without neutral evidence show no score. This score is not the headline positive-share percentage.

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.

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.

Neutral tab

Use Neutral to inspect descriptive mentions that express neither a positive nor a negative judgment.
AI search Sentiment with Neutral selected and descriptive theme evidence
Check whether the description is accurate and whether important differentiators are missing. Clarify incomplete product descriptions with useful source material. Neutral observations do not lower the positive-share metric; a neutral-only result does not establish positive or negative sentiment.

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.

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.

How to use filters

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

What to fix first

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.

Keep in mind

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

Where to go next