Does negative sentiment about your brand hurt AI citations? Have some bad reviews floating around

Sentiment affects HOW you’re presented, not WHETHER you’re cited. Here’s the breakdown:

What AI does with sentiment:

Your situation: Past issue + current fix = Mixed. AI may present both.

What you CAN control:

1. Volume of recent positive content

   • New reviews, testimonials
   • Positive coverage
   • Updated product information

2. Recency weighting

   • AI systems often weight recent information more
   • Fresh positive content can shift the balance

3. Direct acknowledgment

   • Content addressing the old issue
   • Showing how you fixed it
   • Transparency actually helps

What you can’t control:

• Deleting old content from AI training data
• Immediate reputation reset

We’ve recovered brand reputation in AI before. Real experience:

The situation: Client had a data breach 18 months prior. AI always mentioned it.

What we did:

Month 1-2:

• Published detailed “what we learned” transparency content
• Got security certifications
• Third-party security audit published

Month 3-4:

• Secured positive industry press coverage
• Customer case studies post-improvement
• Expert testimonials on new security

Month 5-6:

• Continued positive content flow
• Monitored AI mentions weekly

Result: AI shifted from “had security issues” to “improved security practices” to eventually recommending without caveats.

The key: Don’t hide from the issue. Address it directly and show improvement. AI picks up on the transparency and recovery narrative.

How AI evaluates sentiment:

AI doesn’t have “sentiment scores” per se, but:

• Trains on text that includes sentiment context
• Learns associations between brands and positive/negative language
• Synthesizes from multiple sources

What drives perception:

1. Volume of positive vs. negative content
2. Recency of sentiment signals
3. Authority of sources (TechCrunch negative > random blog)
4. Context and nuance

The balance analogy: Think of it as a scale:

• Old negative content on one side
• New positive content on other side
• AI presents based on which side weighs more

What tips the scale:

• More recent = heavier weight
• More authoritative = heavier weight
• Direct addressing of issues = positive weight

Your job: Add weight to the positive side.

Consider your competitive context:

Relative sentiment matters: AI comparing options considers your sentiment vs. competitors.

Positioning opportunity: If competitors also have issues, your mixed sentiment is less damaging.

Strategy:

1. Monitor competitor sentiment in AI
2. Emphasize areas where you’re stronger
3. Create content differentiating on fixed areas

Be careful: Don’t go negative on competitors - focus on your improvements.

Sometimes mixed sentiment still wins if you’re the most comprehensive option.

Reviews are crucial for AI sentiment:

AI heavily references reviews:

• “According to reviews…”
• “Users report that…”
• “Common feedback includes…”

Review strategy for sentiment recovery:

1. Encourage new reviews

   • Post-purchase review requests
   • Easy review process
   • Follow up with satisfied customers

2. Respond to old negatives

   • Professional responses
   • Show issues were addressed
   • Creates context AI can learn from

3. Highlight specific improvements

   • Guide reviewers to mention fixed features
   • Case studies showing before/after

4. Diversify review platforms

   • Don’t just rely on Google
   • Industry-specific review sites
   • Social proof on LinkedIn

The ratio: Aim for recent positive reviews to outnumber old negatives by 5:1 or better.

Counterintuitive approach: Own the narrative

Instead of hiding from the issue: Create content directly addressing what happened and how you fixed it.

Why this works:

• AI learns the complete story
• Shows accountability
• Creates positive context around negative events
• More authentic than ignoring it

Content template: “[What happened] > [What we learned] > [How we improved] > [Results since]”

Example headline: “From [Issue] to [Solution]: How We Rebuilt [Product Feature]”

The benefit: This content often GETS CITED when AI discusses your past issue. It becomes the authoritative source, reframing the narrative.

Risk: Don’t do this if you haven’t actually fixed the issue. Transparency only works with substance.

How to track sentiment in AI:

What to monitor:

1. How AI describes your brand
2. Sentiment of the language used
3. Whether caveats are included
4. Position relative to competitors

Practical monitoring:

• Weekly prompt testing across platforms
• Document exact phrasing AI uses
• Track changes over time

Am I Cited helps with this:

• Shows how AI describes your brand
• Tracks sentiment patterns
• Alerts when presentation changes

Metrics to track:

• % of mentions that are positive
• % that include caveats
• Trend over time (improving/declining)

Without monitoring, you won’t know if your efforts are working.

This gave me a clear action plan. Summary:

The reality:

• Negative sentiment affects HOW we’re presented
• We can shift perception with recent positive content
• Ratio and recency matter most
• Transparency about the issue can actually help

Our action plan:

Month 1-2:

• Create transparency content about issue and fix
• Push for new reviews from satisfied customers
• Secure 2-3 positive industry articles

Month 3-4:

• Continue positive content flow
• Customer case studies post-improvement
• Third-party endorsements/certifications

Ongoing:

• Monitor AI sentiment weekly
• Track sentiment shift over time
• Adjust based on what’s working

Key strategies:

1. Own the narrative (don’t hide)
2. Outweigh old negative with new positive
3. Focus on recent and authoritative sources
4. Monitor and measure progress

Mindset shift: This isn’t about erasing the past - it’s about giving AI more recent, positive content to balance against it.

Thanks everyone!