How to Track Brand Sentiment in AI Search Results

Key Takeaways

• AI brand sentiment measures the tone ChatGPT, Perplexity, Gemini, and Google AI Overviews use when they describe your company, which is different from what humans say on social media.
• The working rubric is a five-point scale (endorsement, neutral, cautious, negative, hallucination) combined into a Net Sentiment Score that runs from −100 to +100.
• You have three stack options: a free Google Sheet, a DIY scripted workflow, or a dedicated platform like Otterly, Profound, Peec, or HubSpot’s AEO Grader.
• Hallucinations deserve a separate playbook because the fix is not the same as correcting a negative mention.
• 95% of your AI sentiment is shaped by third-party sources, not your own website, so remediation starts with finding and fixing what the model cites.

Your Google ranking is not the only place your reputation lives anymore. When a buyer asks ChatGPT “is Boston Web Marketing a good agency” or “what are the best Boston SEO companies,” the model forms an opinion on the spot. Sometimes it calls you a top pick. Other times it hedges. Occasionally it mentions a weakness you did not know existed. And sometimes it makes something up entirely.

Here’s the uncomfortable part: that opinion directly moves deals. Conductor’s tracking data shows Reddit’s AI citation share dropped 23% in a single month (from 2.02% in October 2025 to 1.55% in November 2025), meaning a brand that looked solid in October can look weak in November for reasons nobody on your team caused. If you are not measuring the tone of these mentions, you are flying blind on a channel that increasingly decides which companies even make the shortlist.

What This Guide Covers

In this post, we cover what AI brand sentiment actually is, how to classify and score every mention, the three tracking stacks you can choose from (free, DIY, enterprise), a remediation playbook with seven tactics, and how local businesses should handle this differently than SaaS.

Who this is for: In-house marketers and SMB owners who keep seeing their brand mentioned in AI answers and want a repeatable way to measure whether those mentions are helping or hurting. Agencies that need a client-ready sentiment report. Businesses that have spotted a hallucination or unfair comparison and want a fix process, not a panic response.

In This Post

1. What AI Brand Sentiment Actually Means
2. The 5-Point Scale and Net Sentiment Score
3. Step 1: Pick the Right Platforms to Monitor
4. Step 2: Build Your Sentiment Prompt Library
5. Step 3: Choose Your Tracking Stack (Free, DIY, Enterprise)
6. Step 4: Classify Every Response
7. Step 5: Map Themes to Business Drivers
8. Step 6: Diagnose the Source Behind Each Mention
9. Step 7: The 7-Tactic Remediation Playbook
10. Handling Hallucinations (A Separate Problem)
11. The Local Business Angle
12. 12 Common Mistakes to Avoid
13. FAQ
14. Turning Sentiment Data Into a Reputation Advantage

What AI Brand Sentiment Actually Means

AI brand sentiment is the emotional tone a language model uses when it describes your company in a generated answer. However, it is not the same thing as social listening. Traditional sentiment tracking (the Brand24 or Mention.com flavor) reads what humans say on Twitter, Reddit, and review sites. In contrast, AI sentiment reads what the model itself generates, which is influenced by those human sources but filtered through the model’s training data, safety layers, and whatever documents it retrieved for that specific query.

That difference matters for two reasons. First, an AI model can describe your brand warmly even when Reddit is roasting you, because the model is blending dozens of sources and smoothing the edges. Second, it can describe you coldly for reasons that have nothing to do with current sentiment. For instance, an outdated review, a missing product page, or a competitor’s well-placed comparison post that the model happens to trust.

Social listening vs AI sentiment tracking

Dimension	Social Listening	AI Sentiment
Source	Human posts and reviews	AI-generated responses
Cadence	Continuous firehose	Prompt-triggered sample
Volume	Thousands per day	Dozens per week
Who sees it	Other humans scrolling feeds	Buyers in the decision moment
Fix lever	Reply, moderate, respond	Change the upstream source the model trusts

The 5-Point Scale and Net Sentiment Score

Most early frameworks used three buckets: positive, neutral, negative. However, that is too crude for AI outputs because it misses two categories that matter more than the others. Therefore, a good working rubric uses five:

• Endorsement: The model recommends you, calls you a top pick, or uses warm language (“trusted,” “well-regarded,” “strong choice”).
• Neutral: You are listed without flavor. Often in a bulleted set of options with no commentary.
• Cautious: Hedged language. Phrases like “some users report,” “may be better suited for,” “worth considering but.”
• Negative: Direct criticism or unfavorable comparison. Competitor gets the praise, you get the caveat.
• Hallucination: The model states something factually wrong about you. Wrong pricing, fake features, invented founders, imagined lawsuits. This deserves its own bucket because the fix is different from the other four.

From there, the common metric is the Net Sentiment Score:

NSS = (Endorsement + Neutral − Cautious − Negative − Hallucination) / Total Mentions × 100

The score ranges from −100 to +100. A score above +40 suggests healthy AI reputation. Between 0 and +40 is typical for most mid-market brands. Below 0 means there is a specific problem to fix, not a general drift.

A few notes on the math. First, we include neutral on the positive side because being listed at all is better than being omitted in an unbranded query, and the absence of negative language is itself a signal. Furthermore, we weight cautious as a negative because hedged language (“some users report,” “may not be the best fit”) moves buyers toward competitors even when the model is technically not criticizing you. Some frameworks exclude cautious and neutral entirely, which produces a more volatile score and can mask meaningful movement.

Step 1: Pick the Right Platforms to Monitor

You cannot monitor every AI tool, and frankly, you should not try. Most B2B brands get 80% of their value from three platforms: ChatGPT, Perplexity, and Google AI Overviews. Consumer brands typically add Gemini. Claude matters for developer audiences, while Copilot matters if your buyers are deeply embedded in Microsoft 365. Google AI Overviews in particular is where most teams see the biggest sentiment swings, and our guide on simple content changes that help you rank in Google’s AI Overviews pairs well with the tracking work in this post.

For most SMBs, a practical starting set looks like this:

• ChatGPT: Largest audience, widest training data, most influential on B2B buyer research.
• Perplexity: Most transparent citations, easiest to audit, best for tracking which sources the model trusts.
• Google AI Overviews: Highest volume of touches, integrated with traditional search, directly tied to your existing SEO.

One wrinkle most guides skip: the same prompt can return different answers based on region, logged-in history, and whether you are in a fresh session. Run your prompts in incognito or from a clean account, and note which region you are testing. A prompt run from a Boston IP will often cite different sources than the same prompt from London, and that matters if your buyers are regionally concentrated.

Pro Tip: Lock your testing conditions the same way you would for an A/B test. Same browser, same VPN exit point, same logged-out state, same time of day. Small variables create noisy sentiment data that looks like real shifts but isn’t.

Step 2: Build Your Sentiment Prompt Library

The goal here is to build a repeatable prompt set you can run every week or every month. In practice, consistency matters more than volume. For example, ten well-chosen prompts run over six months will tell you more than fifty one-off queries.

At a minimum, a strong library mixes four prompt types:

1. Branded prompts (force the model to talk about you). Examples: “Tell me about [Your Brand].” “What is the reputation of [Your Brand]?” “Is [Your Brand] a good [category]?”
2. Unbranded discovery prompts (do you appear at all). Examples: “Best [category] in [city].” “Top [category] for [buyer type].” “Who should I hire for [specific service]?”
3. Comparison prompts (you against specific competitors). Examples: “[Your Brand] vs [Competitor].” “Is [Competitor] better than [Your Brand] for [use case]?”
4. Stress-test prompts (surface problems proactively). Examples: “What are the downsides of [Your Brand]?” “Who should avoid [Your Brand]?” “What do users complain about with [Your Brand]?”

Rule of thumb: 40% branded, 30% unbranded, 20% comparison, 10% stress-test. Adjust toward stress-test if you are in active reputation remediation.

Step 3: Choose Your Tracking Stack (Free, DIY, Enterprise)

Ultimately, you have three real options, and picking the wrong tier is the most common mistake we see. Instead of chasing the loudest vendor, pick based on how many prompts you can realistically run per week.

Tier 1: The Free Spreadsheet Method

Right for solo marketers, SMBs, and generally anyone running 10 to 30 prompts per week.

Build a Google Sheet with these columns: Date, Platform, Prompt, Prompt Type, Raw Response, Sentiment Bucket, Position (first / middle / last / not listed), Top Cited Sources, Notes. Run your prompts manually once a week. Paste the answer into the sheet. Tag the sentiment yourself. Calculate NSS with a simple formula at the bottom.

Let’s be real about this: it sounds primitive and it is. However, it also works better than 90% of teams expect because you read every response yourself, which builds pattern recognition faster than any dashboard. Ultimately, most of the value from sentiment tracking comes from noticing weird answers early, not from charting a slow-moving score.

Tier 2: The DIY Scripted Workflow

Right for technical teams, particularly those running 50+ prompts per week or brands that want evidence archives.

Use Playwright or Puppeteer to query ChatGPT, Perplexity, and Google AI Overviews on a schedule. Store raw responses in a database (Postgres, Airtable, or Google Sheets for a lighter setup). Use OpenAI’s API to classify each response into one of the buckets above. Save screenshots for every query so you have an evidence chain when a response changes.

The cost is your engineering time plus roughly $30 to $80 per month in API fees for classification. On the upside, you can run hundreds of prompts without the manual labor and you own the raw data. The trade-off is that scripts will break whenever the platforms change their UIs, which happens often.

Tier 3: Dedicated Platforms

Right for: enterprise brands, agencies managing multiple clients, any team running 100+ prompts weekly. If you want the setup handled for you, our SEO and AI visibility services can build and run this stack as part of a broader program.

Dedicated AI brand sentiment platforms (April 2026)

Platform	Best For	Notable Strength
Otterly	SMB to mid-market	Clean NSS dashboards, per-response drill-down
Profound	Enterprise B2B	Theme-based sentiment, citation source analysis
Peec AI	SaaS brands	Branded vs unbranded separation, project-based setup
Sight AI	Mid-market, lower-technical teams	Six-dimension scoring, trajectory tracking
Visiblie	Brands doing active remediation	Hallucination tracking, AI Visibility Maturity Model
Conductor	Enterprise with existing Conductor SEO	Sentiment by business driver, crisis alerts
HubSpot AEO Grader	Quick snapshots, free tier	Five-dimension score, no setup required
Semrush AI Visibility Toolkit	Teams already on Semrush	Integrated with traditional SEO data

Step 4: Classify Every Response

Whichever tier you picked, the classification work is where most teams cut corners. Do not skip this step. After all, the difference between “your brand is mentioned 12 times” and “your brand is mentioned 12 times, 3 favorably, 7 neutrally, 1 negatively, and 1 hallucinated” is the difference between a vanity dashboard and a usable signal.

For each response, capture four things:

• Sentiment bucket (one of the five categories above).
• Positional prominence: Listed first, listed in the middle, listed last, or not listed at all.
• Cited sources: Every URL the model links to or references. Perplexity and AI Overviews make this easy. ChatGPT and Gemini sometimes need probing (“what sources did you use for that answer?”).
• Evidence snapshot: A screenshot with a timestamp. Responses change, and you need an archive when a bad one disappears.

Watch Out: Do not outsource classification to the same model you are auditing. ChatGPT classifying ChatGPT responses inherits whatever bias the model has about itself. Use a different model for classification, or keep a human in the loop for the first few months.

Step 5: Map Themes to Business Drivers

Once you have a month of classified responses, patterns will start showing up. For example, you might notice that every negative mention clusters around pricing, or support responsiveness, or one specific product line. That clustering is what Conductor calls a “business driver,” and it is the most actionable layer of the whole framework.

Common driver categories:

• Price: Is the model calling you expensive, cheap, or fair?
• Quality: How does the model describe your core product or service?
• Support: Do responses mention slow service, difficult onboarding, or warm customer success?
• Trust: Is the model citing lawsuits, data breaches, or ethics concerns?
• Fit: Does the model position you as right for certain buyers and wrong for others?
• Recency: Is the model working from outdated information?

Plot each negative or cautious mention against a driver. The driver with the highest count of negative mentions is your fix priority. The driver with the highest count of neutral mentions is your opportunity to convert neutral to positive.

Step 6: Diagnose the Source Behind Each Mention

This is where sentiment tracking stops being a vanity exercise and starts moving the needle. Whenever an AI model says something negative about your brand, it got that from somewhere. Therefore, your job is to trace it back.

First, pull the citations. Then categorize each one:

• Owned: Your website, your blog, your product docs.
• Earned: Press coverage, podcast interviews, conference talks.
• Third-party review sites: G2, Trustpilot, Capterra, Glassdoor.
• Community content: Reddit threads, YouTube videos, LinkedIn posts.
• Competitor content: Comparison posts and “alternatives to” articles published by rivals.

Press Gazette reported in 2025 that Reddit held the top citation spot across many AI answer engines for much of the year, and Profound’s tracking put Reddit’s share around 3.11% at its peak. That share has since declined, but community content is still where a large share of your sentiment actually forms, even though most brands still focus optimization effort on their own site.

Did You Know: Reddit’s AI citation share fell 50% between October 2025 and January 2026 as language models shifted toward fewer, higher-authority sources. Conductor

Step 7: The 7-Tactic Remediation Playbook

Once you know what the model is saying, which driver it clusters around, and which source it came from, remediation becomes a matter of picking the right lever. Below are the seven that actually work, in rough order of speed:

1. Correct the Influential Source Directly

If a G2 review, Trustpilot entry, or third-party blog post contains outdated or wrong information, contact the author or the platform. G2 and Trustpilot both have formal dispute processes. Independent bloggers often accept updates if you email them with new facts. Fixing one high-citation source can move sentiment across dozens of prompts.

2. Refresh Your Owned Content

First, audit the pages on your own site that AI models cite most. If the model is pulling from a page you wrote in 2022, update it. Additionally, add current pricing, current product features, current team, and current case studies. Finally, aim for short paragraphs, clear headings, and direct answers to the questions buyers actually ask.

3. Earn Fresh High-Citation Mentions

Pitch stories to publications your target AI models cite. For example, use Qwoted, Featured, or direct outreach to industry writers (HARO shut down in late 2024, so the free options have narrowed). Notably, a single placement in a frequently-cited publication shifts more sentiment than a month of blog posts on your own domain.

4. Participate in Reddit Threads

First, find the Reddit threads your target prompts cite. Then reply as a company representative when appropriate, correct misinformation, and add helpful context. Never astroturf. The communities catch it immediately and the backlash will do more damage than the original problem.

5. Launch or Update YouTube Content

Profound’s analysis shows that YouTube videos with 1,000 to 100,000 views earn disproportionate AI citations (the median cited video sits around 4,400 views). That is a reachable target for most SMBs. Short, focused videos that answer specific buyer questions often outperform polished brand films for sentiment purposes.

6. Refresh Review-Platform Profiles

Make sure your G2, Capterra, Trustpilot, and Glassdoor profiles are current. Additionally, respond to every negative review professionally. Your response becomes part of the text the AI reads. As a result, a measured, specific reply to a bad review often shifts the net tone of the entire profile in AI eyes.

7. Submit Platform Feedback

ChatGPT, Gemini, and Perplexity all accept feedback on incorrect outputs. Use the thumbs-down button, then include a short correction. Platforms do re-train on aggregated feedback, so a concerted effort over weeks can nudge persistent misstatements.

Handling Hallucinations (A Separate Problem)

Hallucinations deserve their own section because the fix is fundamentally different. Specifically, a negative mention reflects something real that someone said about you somewhere. In contrast, a hallucination reflects nothing. Instead, the model invented it, often by confusing you with a similarly-named company or misremembering training data. We covered the broader playbook in our guide on how to avoid AI misinformation, which is worth reading alongside this section.

Here’s a tight playbook for a hallucination:

1. Document it. Screenshot with timestamp, exact prompt, exact response, platform, logged-in or logged-out state.
2. Reproduce it. Run the prompt two or three more times in fresh sessions. If it shows up once out of ten tries, prioritize accordingly. If it shows up every time, escalate.
3. Submit feedback on the platform. Use the correction feature where available.
4. Publish clear counter-information on your own site. If the hallucination claims you offer a service you do not offer, add a clear page stating what you actually do.
5. For persistent, damaging hallucinations, consult a media or defamation attorney. Platforms have responded to formal legal letters when the false information is specific and damaging.

The Local Business Angle

In general, sentiment tracking content is written for SaaS companies with national reach. However, local businesses play a different game. For instance, a plumber in Dedham or a dentist in Back Bay is not trying to out-rank HubSpot in AI answers. Instead, they are trying to make sure that when a local buyer asks ChatGPT “best plumber near me,” the answer is favorable.

For local brands, the sentiment funnel is shorter but tighter. Similarly, the fix list is shorter because the number of cited sources for a local query is smaller:

• Your Google Business Profile reviews are the single biggest sentiment input for Gemini and Google AI Overviews. A rating above 4.2 with 50+ reviews is the practical entry threshold.
• ChatGPT does not read Google reviews. It reads Trustpilot, Yelp, and whatever the open web says about your business. If you are a local brand with 120 Google reviews and zero Yelp presence, ChatGPT will describe you very differently than Gemini does.
• Regional citation sources matter. Local newspapers, chamber of commerce profiles, and neighborhood forums often cite higher than national sites for local queries.

This is one of the reasons local brands need to get the basics right first. If your Google Business Profile is not fully verified and complete, none of the sentiment work downstream will matter. Our guide on how strict Google Business verification is walks through that foundation.

12 Common Mistakes to Avoid

1. Running fewer than 10 prompts per platform (too small a sample to read signal).
2. Mixing branded and unbranded prompts in the same dashboard (they measure different things).
3. Using only three sentiment buckets (positive, neutral, negative) and missing hallucinations entirely.
4. Outsourcing classification to the same model you are auditing.
5. Not archiving screenshots (responses change, evidence vanishes).
6. Chasing every negative mention (most do not matter, build threshold rules).
7. Ignoring positional prominence (being listed last in a bulleted set is almost invisible).
8. Skipping regional variance (same prompt, different answer, you miss it).
9. Focusing only on your own website instead of the sources the model actually cites.
10. Treating hallucinations the same as negative mentions (the fix is different).
11. Measuring once and calling it a program (you need a cadence, weekly or monthly).
12. Reporting NSS in isolation from traditional KPIs (connect it to branded search, organic SoV, review rating).

FAQ

What is a good Net Sentiment Score?

The bands in Section 2 apply across most categories, but benchmarks vary by industry. Heavily-regulated spaces (finance, healthcare) tend to cluster lower because AI models default to cautious language around compliance-sensitive topics. A score near zero in those verticals is not the same concern it would be in consumer SaaS.

How often should I check AI brand sentiment?

Weekly during active remediation. Monthly for steady-state tracking. Real-time alerts for any prompt that returns a hallucination or a sharp drop below zero. Running less frequently than monthly means you will miss shifts while they are still fixable.

Can I track AI brand sentiment for free?

Yes. The spreadsheet method above costs nothing and works well for SMBs running 10 to 30 prompts per week. HubSpot’s AEO Grader also offers a free snapshot. The ceiling of free is low, but the floor is higher than most teams would expect.

Does AI sentiment affect where I rank in traditional search?

Not directly, but the sources that shape AI sentiment (reviews, citations, third-party content) are the same sources that shape traditional search authority. Improvements to one often improve the other. The Semrush AI Visibility Toolkit is a useful bridge tool for teams that want both views in one dashboard.

How is AI sentiment different from social listening?

Social listening reads humans. AI sentiment reads machines reading humans. The distance matters. AI models smooth over outliers and can present a brand warmly even when a social firehose is rough, or cold when Twitter is calm. You need both because they fail in different directions.

Which AI platform matters most for my brand?

ChatGPT has the largest audience and the widest training data, so it is the safest first priority for most B2B brands. Google AI Overviews has the highest touch volume because it is integrated with standard search. Gemini matters most for local businesses because of its direct Google Maps access. Perplexity matters most for research-heavy audiences and for auditing your citations.

Can AI models be legally wrong about my brand?

Yes, and it happens more than most businesses realize. If a model states a fact about you that is false and damaging (a made-up lawsuit, an invented product recall, a false pricing claim), you have options. Start with the platform’s feedback channel. For persistent or high-damage cases, a media attorney can escalate.

How do I fix a bad mention once I find one?

Trace the source first. For a third-party review, fix it at the source. When the model is pulling from outdated content on your own site, refresh the page. If a competitor’s comparison post is driving the mention, earn a fresh counter-narrative in a frequently-cited publication. On Reddit, reply transparently as a company representative rather than astroturfing. For a hallucination, use the platform’s feedback channel and document aggressively.

Turning Sentiment Data Into a Reputation Advantage

Five years ago, your reputation lived in Google’s top ten results and your review-site ratings. Today, it also lives in whatever ChatGPT tells a buyer when they ask about your category. Meanwhile, the brands that are measuring this now are building the muscle to shape it. The ones that wait will spend the next two years explaining to their leadership why they did not see the shift coming.

The good news is that the work is not exotic. A spreadsheet, a fixed prompt set, a weekly cadence, and a habit of tracing bad mentions back to their source will get most SMBs 80% of the value of an enterprise platform. The tactics in Step 7 give you a concrete path from “our AI sentiment is weak” to “our AI sentiment is improving month over month.”

Boston Web Marketing helps businesses across Boston and New England audit their AI brand sentiment, map the source structure behind each mention, and build remediation plans that move the score. We combine this with traditional SEO and reputation management work so the same effort compounds across every channel your buyers actually use. Contact Boston Web Marketing today to get started.

Boston Web Marketing
1 McKinley Square, Boston, MA 02109
857.349.5062

Request a Free AI Brand Sentiment Audit