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Sentiment measures how AI talks about you when it mentions your brand. Getting mentioned isn’t enough — what matters is whether AI recommends you enthusiastically or with hesitation.

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Understanding Sentiment

When AI mentions your brand, it doesn’t just list your name — it describes you. That description carries an opinion, and that opinion shapes how users perceive you before they ever visit your site. Here’s the same brand mentioned with different sentiment: 
Positive Sentiment:
"For small agencies, Acme CRM is an excellent choice. Users consistently
praise its intuitive interface and responsive support team. It's
particularly well-suited for teams that prioritize ease of use."

Negative Sentiment:
"Acme CRM is an option to consider, though some users report frustration
with customer support response times. The learning curve can be steep
for non-technical teams, and pricing has increased significantly."

Neutral Sentiment:
"Acme CRM offers contact management, pipeline tracking, and email
integration. It serves small to medium businesses in various industries."
Same brand, three very different impressions. The user reading the positive version is primed to convert. The user reading the negative version is primed to look elsewhere.

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Why Sentiment matters

In traditional search, users read your content and form their own opinion. In AI search, AI forms the opinion for them. The way AI describes you directly shapes perception before users click through. The impact on conversion:
SentimentUser behaviorBusiness impact
PositiveUsers arrive pre-sold, ready to convertHigher conversion rates, shorter sales cycles
NeutralUsers arrive curious, need convincingNormal conversion rates, typical sales process
NegativeUsers arrive skeptical or don’t click at allLower conversions, objection-heavy sales calls
You can have high Share of Voice with low Sentiment — meaning you’re mentioned often but described poorly. This is worse than not being mentioned at all, because you’re actively being positioned negatively in users’ minds.

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How Sentiment is calculated

Sentiment is analyzed directly from AI responses. When you track a prompt, Attensira sends it to multiple AI platforms and analyzes the actual response text to score how positively or negatively each model describes you.

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Sentiment scoring

Each response is scored on a 0-100 scale based on the language used:
ScoreLabelWhat AI typically says
80-100Very Positive”Excellent,” “highly recommend,” “users love,” “best-in-class”
70-79Positive”Great option,” “strong choice,” “well-regarded,” “popular”
50-69Neutral”An option,” “offers features,” factual descriptions without opinion
30-49Negative”Some concerns,” “users report issues,” “can be challenging”
0-29Very Negative”Avoid,” “significant problems,” “better alternatives exist”

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How it works in practice

For each prompt you track, we get responses from multiple AI platforms and score each one: 
Prompt: "What's the best CRM for marketing agencies?"

Responses:
├── ChatGPT response: "Acme CRM is excellent for agencies..." → Score: 85
├── Perplexity response: "Acme is a solid choice with good..." → Score: 78
├── Claude response: "For marketing agencies, Acme offers..." → Score: 72
├── Gemini response: "Acme CRM is well-regarded among..." → Score: 80
└── Grok response: "Acme has strong agency features..." → Score: 76

Average for this prompt: (85 + 78 + 72 + 80 + 76) / 5 = 78.2
Your overall sentiment is the average across all prompts and all platforms. A score below 50 indicates more negative mentions than positive.

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Breaking down your sentiment

You can view sentiment in multiple ways:
  • Overall sentiment — Average across all prompts and platforms
  • Sentiment by platform — How each AI model perceives you (ChatGPT might score you 82, while Perplexity scores you 68)
  • Sentiment by prompt — Which topics generate positive vs. negative responses
  • Sentiment over time — Track how perception changes week to week

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What drives Sentiment

AI forms opinions about your brand based on what it reads across the web. Key sources:

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1. Review platforms

Your ratings on G2, Capterra, TrustRadius, and similar sites directly influence how AI describes you. A 4.8-star rating leads to “highly rated” descriptions. A 3.2-star rating leads to “mixed reviews.” 
High review scores → "Users consistently rate Acme highly..."
Low review scores → "Acme receives mixed reviews, with some users noting..."

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2. Social media and forums

Community sentiment on Twitter/X, Reddit, LinkedIn, and industry forums shapes AI’s perception. Frequent complaints = negative sentiment. Enthusiastic users = positive sentiment.

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3. Media coverage

How publications write about you matters. Positive feature articles boost sentiment. Critical coverage or negative news stories drag it down.

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4. Customer testimonials and case studies

Published success stories and testimonials give AI positive language to draw from. Without them, AI has less positive material to reference.

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5. Comparison content

When third parties compare you to competitors, who wins? If comparison articles consistently favor competitors, AI absorbs that positioning.

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Sentiment vs. other metrics

Understanding how Sentiment interacts with your other metrics:
ScenarioWhat it meansPriority action
High Share of Voice + High SentimentIdeal state — you’re mentioned often and positivelyMaintain and expand
High Share of Voice + Low SentimentDangerous — you’re visible but described negativelyFix perception urgently
Low Share of Voice + High SentimentHidden gem — those who know you love youIncrease awareness
Low Share of Voice + Low SentimentSignificant work needed on both frontsAddress sentiment first, then visibility
High Position + Low SentimentYou rank well but with caveatsImprove how you’re described
Low Position + High SentimentDescribed well when mentioned, but mentioned lateWork on position factors
If you have high visibility but low sentiment, fixing sentiment should be your top priority. Being mentioned negatively is actively hurting you.

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Common Sentiment patterns

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The “but” problem

AI often uses “but” to introduce concerns: 
"Acme CRM is feature-rich, but users report a steep learning curve."
"Acme offers good value, but customer support can be slow."
Track what comes after the “but” — these are your sentiment killers.

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The comparison trap

AI often establishes sentiment through comparison: 
Positive: "Unlike some competitors, Acme offers excellent support."
Negative: "While competitors offer free tiers, Acme's pricing starts at $99."
How you compare to competitors directly affects your sentiment.

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The qualifier issue

Watch for qualifiers that soften recommendations: 
Strong: "I recommend Acme for agencies."
Weak: "Acme could be worth considering for some agencies."
Weaker: "If budget isn't a concern, Acme might work."
More qualifiers = lower sentiment, even if the overall tone seems positive.

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Improving your Sentiment

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Step 1: Diagnose the problem

Identify which mentions are dragging down your score:
  1. Filter by sentiment — Find your lowest-scoring mentions
  2. Identify patterns — What concerns appear repeatedly?
  3. Trace to sources — Where is AI getting this negative information?
Common negative patterns:
  • Support complaints
  • Pricing concerns
  • Feature gaps vs. competitors
  • Reliability issues
  • Learning curve complaints

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Step 2: Address root causes

Sometimes sentiment reflects real problems. If users consistently complain about support, improving support will eventually improve sentiment. Real improvements that affect sentiment:
  • Fix product issues that generate complaints
  • Improve customer support response times
  • Address common feature requests
  • Make onboarding easier
  • Adjust pricing if it’s a consistent pain point

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Step 3: Generate positive signals

Create content and experiences that give AI positive language to draw from: Review management:
  • Ask satisfied customers to leave reviews
  • Respond professionally to negative reviews
  • Keep review profiles current and complete
  • Aim for recent reviews (AI may weight recency)
Content creation:
  • Publish customer success stories
  • Create case studies with specific results
  • Share testimonials on your site
  • Get featured in positive “best of” lists
PR and media:
  • Pursue positive media coverage
  • Respond to negative coverage with corrections
  • Share company wins and milestones
  • Earn industry awards and recognition

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Step 4: Monitor changes

Sentiment changes slowly — it takes time for new content to be indexed and for AI models to update their understanding. Track sentiment over weeks and months, not days. 
Sentiment improvement timeline:
├── Week 1-2: New reviews/content published
├── Week 2-4: Content gets indexed
├── Week 4-8: AI models begin incorporating new data
└── Week 8+: Sentiment scores start reflecting changes

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Sentiment by platform

Different AI platforms may perceive you differently based on their data sources:
PlatformPrimary sentiment sources
ChatGPTBroad web content, tends toward consensus view
ClaudeMay weigh nuanced analysis, looks at full context
PerplexityHeavily citation-based, recent sources matter most
GeminiGoogle’s index, review sites weighted heavily
GrokReal-time X/Twitter sentiment, social signals
CopilotBing-indexed content, LinkedIn signals, enterprise sources
Google AI ModeGoogle’s index, review sites, Knowledge Graph data
Google AI OverviewGoogle’s index, high-authority sources, featured snippet data
If your sentiment differs significantly across platforms, investigate which data sources each platform emphasizes. A platform showing lower sentiment may be drawing from sources with more negative content about you.

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Sentiment for different mention types

Not all mentions carry equal weight for sentiment analysis:
Mention typeSentiment impact
Primary recommendationHigh impact — this is where sentiment matters most
Alternative mentionMedium impact — “Also consider” mentions
Comparison mentionVariable — depends on how comparison is framed
Feature mentionLower impact — often neutral, factual
Warning mentionHigh negative impact — actively steering users away
Focus on improving sentiment in your primary recommendations first, as these have the highest conversion impact.

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Next steps

Share of Voice

How often you’re mentioned overall

Sources

Where AI gets its information about you