What it is
The Sentiment tab analyzes how AI engines describe your brand and competitors.
It classifies contextual language into:
Positive
Neutral
Negative
Sentiment insights help you understand not just whether your brand appears, but how it is framed.
This article explains how to use sentiment data strategically for brand monitoring.
Why it matters
AI search engines are increasingly shaping:
Brand perception
Buyer research journeys
Product comparisons
Purchase decisions
If AI models describe your brand negatively, or frame competitors more favorably, it can influence user trust and selection.
Monitoring sentiment helps you:
Detect emerging reputation risks
Identify recurring narrative themes
Compare tone across competitors
Track perception trends over time
Step 1: Monitor overall sentiment trends
Start by reviewing:
Sentiment score
Positive vs neutral vs negative keyword distribution
Sentiment over time chart
Look for:
Gradual declines
Sudden spikes in negative keywords
Persistent neutral framing
What stable sentiment looks like
Consistent positive distribution
Minor weekly fluctuations
No sudden negative spikes
What concerning sentiment looks like
Sustained negative keyword growth
Sharp week-over-week decline
Engine-specific negative framing
When this happens, investigate further.
Step 2: Analyze sentiment by source
Sentiment is split between:
Web Grounding (live web content)
Training Data (model knowledge)
This distinction matters.
If negative sentiment is driven by Web Grounding
Likely causes:
Recent negative reviews
Critical blog posts
News coverage
Forum discussions
Action steps:
Identify cited domains
Address misinformation
Improve authoritative content
Update messaging
If negative sentiment is driven by Training Data
This may reflect:
Long-term brand associations
Historical positioning
Persistent industry narratives
Action steps:
Increase authoritative content presence
Improve brand clarity
Strengthen structured positioning
Training Data perception changes more slowly than Web Grounding.
Step 3: Identify recurring negative keywords
Review:
Negative keyword list
Frequency of mentions
Associated AI engines
Ask:
Are the same concerns repeated?
Are competitors framed more positively?
Are outdated issues resurfacing?
Example patterns
Recurring phrases like:
“Limited features”
“Expensive compared to competitors”
“Steep learning curve”
These signal messaging gaps.
Address them by:
Updating landing pages
Clarifying differentiation
Publishing comparison content
Improving FAQs
Step 4: Compare sentiment across competitors
In the Sentiment tab, compare:
Sentiment score by brand
Positive vs negative distribution
Engine-specific perception
Look for:
Competitors with stronger positive framing
Brands associated with specific advantages
Brands consistently framed negatively
Strategic implications
If competitors are described with:
“Industry leader”
“Most reliable”
“Top choice”
And your brand is described neutrally: Strengthen positioning clarity.
If competitors carry negative associations: Highlight differentiation.
Step 5: Monitor engine-specific perception
Sentiment can vary across AI engines.
You may find:
Positive on Gemini
Neutral on ChatGPT
Slightly negative on Perplexity
This reflects:
Engine grounding behavior
Source weighting differences
Model-specific bias
Avoid assuming sentiment is uniform across engines.
Step 6: Track sentiment over time
Use:
7d view for short-term changes
30d+ view for structural shifts
Do not overreact to 24h changes.
Look for:
Sustained negative trend
Multi-engine shifts
Correlation with citation changes
Turning sentiment insights into action
To improve negative sentiment
Address recurring negative keywords directly
Update content to clarify misconceptions
Strengthen feature comparison pages
Improve documentation clarity
Increase positive third-party coverage
To strengthen positive sentiment
Reinforce positive keywords in messaging
Expand case studies and testimonials
Improve structured summaries
Highlight differentiators clearly
To prevent reputation risk
Monitor weekly trends
Set internal review cadence
Investigate spikes immediately
Compare against competitor sentiment shifts
Common mistakes to avoid
Mistake 1: Ignoring neutral sentiment
Neutral framing may indicate weak positioning.
Mistake 2: Reacting to single-run sentiment
AI outputs are probabilistic. Use trends.
Mistake 3: Assuming sentiment equals ranking
Sentiment affects perception, not directly ranking.
Mistake 4: Ignoring source type
Web-grounded negative sentiment is usually actionable faster.
How sentiment fits into AI visibility strategy
Sentiment influences:
Trust perception
Competitive comparison framing
Brand authority
Narrative dominance
Combine sentiment analysis with:
Citation insights
Share of visibility
Engine comparison
Topic performance
Sentiment shows how you are described.
Visibility shows how often you are seen.
Together, they define your AI brand presence.