How I Use Sentiment Delta

I would measure the gap between the positioning I intend and the description AI engines actually return, and I would spend to close that gap before I spent to be mentioned more often. The gap is sentiment delta. On evaluative queries, a strongly negative answer converts at about half the rate of a positive one. Showing up inside the wrong sentence is a revenue problem, not a branding exercise.

The definition and the four-type method I would follow are in What Is Sentiment Delta? | MR Research. That page is the deeper method. This essay is how I would use it as a founder: which queries I would score, which sources I would blame, and what would make me stop treating a negative description as the priority.

Four gaps, kept separate

Sentiment delta is the distance between intended positioning and the description an engine returns. The research page separates four types, and I would log them as four rows.

Directional: the frame points the wrong way. The company says enterprise-grade. ChatGPT says it is a fit for small teams.

Categorical: the market is wrong. The company sells measurement for communications. Perplexity files it under social analytics.

Omission: the brand is absent on a query it should be able to answer. This is the one founders notice, because it is binary.

Attribute: the emphasis is wrong. The differentiator is public pricing. Gemini leads with a Salesforce integration.

Zero gap is rare. Engines draw 59 to 86 percent of citations on consideration queries from earned media, not from the About page. I would assume the About page is our draft and the earned record is the draft the engine prefers. The names for these gaps sit in the glossary. How I would sequence authority, entity, and measurement around them is on Machine Relations. I coined Machine Relations in 2024 so this work would have a name a budget could attach to. Sentiment delta is one of the three metrics I would report with it, beside share of citation and whether the entity resolves. Share of citation says how often we appear. Resolution says whether the engine has the right company. Sentiment delta says what it says when it does.

What the conversion table is worth

Attrifast joined AI sessions to Stripe checkouts for about 200 sites through late 2025 and the second quarter of 2026. On bottom-funnel evaluative queries, "is X worth it" and "X versus Y," conversion against a positive baseline looked like this:

AI sentiment	Conversion versus baseline
Strongly positive	1.00x
Positive	0.94x
Neutral	0.78x
Negative	0.61x
Strongly negative or false	0.49x

A strongly negative or false answer, at 0.49 times, cuts conversion roughly in half against a strongly positive one, on the queries where someone is deciding to buy. The engine matters inside that table. ChatGPT-referred sessions convert at roughly 1.5 to 1.6 times Google organic when the sentiment is positive. Perplexity runs at 1.7 to 2.0 times when positive, and drops sharply when the sentiment turns. Same source family, same intent, different description, different checkout rate.

Attrifast puts a dollar sketch on a stale claim: a B2B software company with 1,800 monthly AI-attributed evaluative sessions and positive-answer revenue per visit of $0.84 loses about $2,160 a year for every 25 percent of that traffic pulled from positive to negative by one stale claim. One claim, one engine. I would treat $2,160 as the illustration's arithmetic, not as our forecast, and I would redo it with our session count and our revenue per visit before I quoted it internally.

Why another blog post on our domain is the wrong fix

Ahrefs, on 75,000 brands, found branded web mentions correlating at 0.664 with appearance in AI Overviews, against 0.218 for backlinks, Spearman. Mentions were the strongest factor in that study and backlinks among the weakest. Ahrefs cautions that the coefficients are not a ratio and that correlation is not causation. I would still rather fund mentions than another backlink campaign if those are the two options the study can speak to.

The same body of Ahrefs work, as summarized in a tactics writeup, found a twelvefold citation gap: 169 AI Overview citations for the top quartile of brands by earned mentions, 14 for the next quartile. Domains with a Domain Trust score above 90 earn 4 times the AI citations of domains below 43.

Hexagon's read of 100,000 AI citations puts third-party editorial mentions at 3.2 times more predictive of AI citations than on-site content volume. Brands with a Wikipedia page or a Wikidata record receive 4.7 times more Perplexity citations than brands without that entity record.

FrictionAI separates recognition from recommendation. A brand can hold the highest Knowledge Graph score in its category and still be recommended at 3.4 percent. A brand with one seventy-ninth of that Knowledge Graph score reached a 92.5 percent recommendation rate. The writeup attributes 97 to 100 percent of AI recommendations to third-party sources. I would not use Knowledge Graph score as a proxy for being the recommended vendor. Recognition is the graph. Recommendation is what other people wrote.

Concentration shows up in one vertical and I would keep it there. Hexagon found the top 2 percent of e-commerce brands capturing 78 percent of AI search recommendations in that category, across ChatGPT, Perplexity, Claude, and Google AI Overviews. That is a recommendation-stage, single-vertical figure. A wide sentiment gap is still a signal that we are far from the sources engines prefer in our own category. Our website, alone, does not close it.

The audit I would run before the quarter's plan

I would pick five queries a buyer asks before they know our name. Best in the category for the use case. Whether the product is worth it. Us against alternatives. The category's actual buying question. I would skip the brand-name query for this audit, because omission and distortion on the way in are the expensive rows.

I would run each query on ChatGPT, Perplexity, Claude, and Google AI Overviews. Citation divergence between ChatGPT and Perplexity is reported at 89 percent. One engine is a sample of the minority.

I would score the language, not the logo. Plus 2, strongly positive, recommended as the choice. Plus 1, positive with a minor caveat. Zero, balanced. Minus 1, a real caveat in the lead. Minus 2, strongly negative or false. Then I would compare the score with the positioning we claim. "Most transparent pricing" against "pricing not publicly available" is a directional gap. I would write the sentence down.

For every zero or negative, I would open the cited URLs. The same source reports that 73 percent of AI citations are ghost citations, a URL present while the brand name never appears, and that named mentions have a 40 percent higher likelihood of resurfacing across consecutive queries. If the engine is citing a page that frames us wrongly, that page is the remediation target. A ghost citation is a reason to get named inside a source the engine already trusts.

The pass takes about half an hour. I would do it before I approved another month of undifferentiated content.

What I would fund to close a row

The hierarchy I would spend against puts third-party mentions first, at the 0.664 Spearman from Ahrefs, news and trade publications next, and owned content third. Language framing, in the Machine Relations research, can shift LLM recommendation rates by up to 334 percent. The words a third party uses, the attributes they name, and whether they recommend, are inputs to the answer. A generic mention leaves the gap open. A mention that states the attribute we are mis-scored on is the one I would pitch.

Attrifast's cadence is the one I would copy: daily during a live reputation event, weekly after a pricing or positioning change, monthly in a steady state, and weekly for four weeks after a remediation to see whether the score moves. The timeline I would tell the board, for an organic earned-media approach, is 4 to 6 months, shorter when the placements are in high-authority publications and the copy addresses the specific gap.

Owned content still has a job. It is the page the third-party claim should agree with. It is a weak tool for overwriting a third party the engine already prefers.

Which gap I would fund first

The four types do not deserve the same quarter, and the conversion table is what sorts them.

I would fund a false or strongly negative sentence first. Attrifast's join, about 200 sites, puts that bucket at 0.49 times the positive baseline on evaluative queries, with a merely negative answer at 0.61 times, neutral at 0.78, positive at 0.94, and strongly positive at 1.00. Half the checkouts, on the queries where someone is deciding, is a larger leak than an omission on a query nobody was ready to buy from. Perplexity's positive sessions at 1.7 to 2.0 times Google organic, and ChatGPT's at 1.5 to 1.6 times, are the upside of getting the sentence right on the engines that already send the visit. The $2,160 sketch, 1,800 sessions at $0.84, one quarter of the traffic pulled down by one stale claim, is the arithmetic I would rerun with our numbers. I would not wait for the rerun to correct a sentence that is simply untrue.

Directional and attribute gaps are the next invoices, and they are earned-media invoices. Engines draw 59 to 86 percent of consideration-query citations from earned media. Ahrefs, 75,000 brands, puts branded mentions at a 0.664 Spearman with AI Overview appearance and backlinks at 0.218, with the caution that those coefficients are not a ratio and not causation. Hexagon's 100,000 citations put third-party editorial mentions at 3.2 times more predictive than on-site volume, and a Wikipedia or Wikidata record at 4.7 times the Perplexity citations of brands without one. Language framing, in the Machine Relations research, can move recommendation rates by as much as 334 percent. I would pitch the attribute we are mis-scored on, in a publication the engine already cites, and I would score the same query weekly for four weeks. A generic mention leaves the 73 percent ghost-citation problem in place: a URL in the answer, our name nowhere in the source. Named mentions, with a reported 40 percent higher chance of resurfacing, are the mentions I would count.

Categorical error is an entity problem wearing a sentiment score. If Perplexity has us in the wrong market, more adjectives in the right market will not move the file. I would want the third-party sentence to name the category, and I would want our own pages to say the same category so the engine is not choosing between drafts. FrictionAI's split is the reason I would not accept a Knowledge Graph gain as a substitute: a category leader on that score recommended 3.4 percent of the time, a brand with a seventy-ninth of the score recommended 92.5 percent of the time, and 97 to 100 percent of recommendations attributed to third parties. Recognition and recommendation are different systems. Omission, the absence, is what I would measure and not panic-buy. Silence converts nothing from that answer. A present, false answer can convert at 0.49 times and teach the wrong lesson. I would still rather be described accurately than be missing. I would not buy empty presence to get there.

The 89 percent citation divergence between ChatGPT and Perplexity is why a fix that shows up on one engine is a lead. The 4 to 6 month organic horizon is why I would not call the quarter a failure at week three. The steady-state cadence, monthly, with weekly checks after a pricing change and daily checks during a live reputation event, is the operating rhythm. Hexagon's 78 percent of e-commerce recommendations captured by the top 2 percent of brands stays in that vertical. I would not import it as a law. I would import the mechanism: third-party framing is the input the answer is built from.

The decision

I would score the five buyer queries across the four engines, log each gap as directional, categorical, omission, or attribute, and fund earned coverage that names the attribute we are getting wrong. I would pause any program whose only success metric is mention rate until the evaluative-query scores are at least neutral.

Scope: evaluative queries, the four engines above, and the cited URLs behind any score of zero or below. The Attrifast multiples are the reason the scope is evaluative traffic and not a vanity query of our own name. The Ahrefs, Hexagon, and FrictionAI results are the reason the spend goes to third parties. The 4-to-6-month band is the review horizon I would set, with a four-week check after each placement meant to close a named gap.

What would change my mind: an evaluative-query sample, on our sessions, where strongly negative answers converted at or above the positive baseline. The 0.49 times figure is from about 200 sites, not from our checkout. If our data disagreed, I would follow our data and keep the audit, because a false description is still a false description. I would also change the "owned content cannot close this" rule if a controlled change on our domain, with no new third-party coverage, moved the score on more than one engine. The 89 percent divergence says I should not trust a movement that appears on only one. A Knowledge Graph increase that left recommendation rate near FrictionAI's 3.4 percent example would not count as closing the gap.

The $2,160 illustration changes no budget by itself. Our session count times our revenue per visit, on the share of evaluative traffic sitting in negative answers, is the number I would take to a planning meeting.

FAQ

What would I put on the monthly scorecard?

The four gap types, the score on each engine for the five queries, and the URL behind any negative. Share of citation can rise while the description stays wrong. I would not let a presence chart retire this one. The research page is the method. The scorecard is the log of our queries.

Does sentiment move revenue, or only reputation?

On Attrifast's join of AI sessions to Stripe, for about 200 sites, strongly negative or false answers converted at 0.49 times the positive baseline on evaluative queries. Positive Perplexity sessions ran at 1.7 to 2.0 times Google organic. I would recompute with our checkout before I used their dollar sketch. I would not wait for that recompute to fix a sentence that is simply false.

How long would I wait before judging a placement?

Weekly for four weeks after the placement, against the specific query and the specific gap it was bought to close. The broader organic horizon in the sources I am using is 4 to 6 months. A single engine moving, against 89 percent divergence between ChatGPT and Perplexity, is a lead. It is not a closed gap.

What if we are absent rather than described badly?

That is omission, and it is the type I would not confuse with a directional gap. Absence converts nothing from that answer. A present, strongly negative description converts at 0.49 times in the Attrifast table, which can be worse than silence on a query where the buyer is ready to purchase. I would still rather be described accurately than be absent. I would not buy empty mentions to get there. Named mentions, on the 40 percent resurfacing figure, are the mentions worth pitching.