# How to Fix Negative Brand Sentiment in AI Answers

> Fix negative AI brand sentiment in six steps: find the source the engine relied on, correct it, publish clear facts, then re-test on a fixed prompt set.

- Author: Sheetal Shakshi, Founder's Office, Nurdd
- Published: 2026-11-01
- Publisher: Noma by Nurdd (https://nomahq.app)
- Original page: https://nomahq.app/blog/fix-negative-ai-brand-sentiment

To **fix negative AI brand sentiment**, find the exact negative statement an engine makes about you, trace it to the page it relied on, correct that page or publish clearer facts that outweigh it, and re-test the same prompts over several weeks. There is no support line for an opinion in an answer, so the repair happens at the source.

## Key takeaways

- Negative sentiment is usually a handful of repeated claims, each traceable to a few pages. Fix claims, not the overall mood.
- Sort every claim into one of four types before acting: factually wrong, outdated, true but unframed, or true and unflattering. Each needs different work.
- Correct your own pages first, then third-party pages, then add new quotable facts. Do not expect a feedback button to change an answer.
- Sample each prompt several times per engine. [Ahrefs, October 2026](https://ahrefs.com/blog/how-stable-are-ai-citations/), found at least half of citations vanish by the next answer, so one re-check proves nothing.
- Record the before state first. Without a dated baseline you cannot say the fix worked.

## Why does AI describe my brand negatively?

An AI engine describes a brand from what it can retrieve and what it learned in training. When it answers a buyer question, it often runs live searches, reads a few pages and writes a summary. If the pages it reads are a two-year-old review, a complaint thread or a competitor's comparison, the summary inherits their tone.

Google says the way its Search finds and processes pages ["remains the core of how our AI systems access your data" (Google Search Central, July 2026)](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). The same logic holds for other engines: the answer is built from pages, so the answer changes when the pages change. Our explainer on [how AI engines decide which sources to cite](https://nomahq.app/blog/how-ai-engines-choose-citations) covers the selection side.

Four causes account for most negative answers:

- **Wrong facts**: a stale price, a feature you do not have, a merged identity with a similarly named company.
- **Outdated facts**: a problem you fixed, still described as current.
- **Thin owned content**: your site does not state the claim, so the engine fills the gap from reviews or competitors.
- **Real criticism**: customers genuinely report a problem, and the engine repeats it.

For background on how the score itself is produced, see [what brand sentiment in AI answers is](https://nomahq.app/blog/what-is-brand-sentiment-in-ai-answers). This guide starts where that one stops: you have a bad answer and want it changed.

## How do I fix negative sentiment about my brand in ChatGPT?

Work through six steps in order. Skipping to publishing before you have traced the source is the most common way teams waste a month.

### Step 1: Capture the exact answers before you change anything

Write down ten to twenty prompts where the negative framing appears. Include unbranded buyer prompts ("best X for Y") and branded ones ("is Brand any good", "Brand vs Competitor"). Run each on every engine your buyers use. Save the full answer text, the engine, the date and every cited URL.

Run each prompt more than once. [Ahrefs, October 2026](https://ahrefs.com/blog/how-stable-are-ai-citations/), tracked 37,500 citations and found that 50 to 83 per cent were gone by the next day, though about 75 per cent of those returned within 20 days. It also reported that ChatGPT repeated exactly the same fan-out searches as the previous day only 26 per cent of the time. One answer is a sample, not a verdict.

### Step 2: Split each answer into individual claims

"Users complain about support" is one claim. "Pricing is expensive" is another. "Lacks an integration with Salesforce" is a third. List each one in a sheet with the engine, prompt and how often it appeared across your repeat runs. A claim that shows up in 8 of 10 runs deserves attention before one that shows up once.

### Step 3: Trace every claim to the page it relied on

Read the cited URLs. Where an engine gives no citation, search for the claim's distinctive wording in a normal search engine; the source is often on the first page. Look for four patterns:

- One page cited across many prompts: fix that page and many answers move.
- A review or forum thread: the claim is probably true to someone, so plan a response and a product or service fix.
- A competitor's comparison page: it may be self-serving or stale, and you can publish a fair one of your own.
- Your own page: the quickest fix, and often the one teams overlook. Old pricing pages, archived blog posts and press releases are common culprits.

Noma by Nurdd lists the sources each answer cites, per engine, so this tracing step can be read from a dashboard instead of rebuilt by hand in a spreadsheet.

### Step 4: Triage each claim by type

The type decides the action. Use this table.

| Claim type | Example | Fix | Who owns it |
| --- | --- | --- | --- |
| Factually wrong | States a feature you never had | Correct your own page; request correction from the publisher; publish a clear factual statement | Web or product marketing |
| Outdated | Describes a bug fixed last year | Publish dated, current facts; update or retire old pages; ask the third party to update | Content and PR |
| True but unframed | "Pricey" with no context | State what the price includes, with a comparison table and dated figures | Product marketing |
| True and unflattering | Slow support responses | Fix the operation first, then publish evidence and respond openly where the criticism sits | Customer success |

The last row matters. If the criticism is accurate, content cannot hide it for long. Engines read new pages as well as old ones, so the durable fix is the underlying problem plus honest evidence that it changed.

### Step 5: Correct and publish at the source

Do the work in this order:

1. **Your own pages.** Fix wrong facts, update dates and remove contradictions between pages. Check that crawlers can reach the corrected pages.
2. **Third-party pages.** Email the publisher with the specific sentence, the correct fact and a public link that proves it. Directories, review profiles and marketplace listings are usually the easiest to update.
3. **New factual pages.** Publish a page that states the claim you want engines to find: what you are, who you serve, current pricing, security, integrations, how you handle the criticised area. Put each fact in a standalone sentence or table with a date.
4. **Community and review sites.** Reply to threads that are cited, openly and within each platform's rules, with your connection disclosed. Ahrefs' analysis of 1.4 million ChatGPT prompts found Reddit pages appear often in retrievals but were [cited only 1.93 per cent of the time (Search Engine Journal, April 2026)](https://www.searchenginejournal.com/chatgpt-often-retrieves-but-rarely-cites-reddit-pages-data-shows/572243/), so confirm a thread is actually cited for your prompts before spending time on it.

Do not use fake reviews, sockpuppet accounts or hidden text. They violate platform rules and the correction record they create can make things worse.

Quotable, specific content helps. The GEO research paper reported that adding relevant statistics, quotations and citations [can boost visibility in generative engines by up to 40 per cent (Search Engine Land, February 2024)](https://searchengineland.com/generative-engine-optimization-framework-introduced-research-paper-435855). Write corrected facts in that form: a named subject, a number, a date and a source.

### Step 6: Make sure engines can read the fix

A corrected page does nothing if crawlers are blocked. OpenAI's documentation says sites [opted out of OAI-SearchBot will not be shown in ChatGPT search answers (OpenAI, October 2026)](https://developers.openai.com/api/docs/bots). Run your corrected pages through the [free Noma AI SEO Checker](https://nomahq.app/tools/ai-seo-checker), which shows whether ChatGPT, Claude, Perplexity and Google AI can read the page and lists the top fixes. The [free AI Crawler Checker](https://nomahq.app/tools/ai-crawler-checker) tests the specific bots against your robots.txt.

## Why is the answer not changing after I fix the page?

Three delays stack up. Crawlers must revisit the page. The engine's retrieval must pick it over the older source. Models that answer from training data may not change until a new version ships. [A new model release](https://nomahq.app/blog/new-ai-model-release-brand-visibility) can also shift answers independent of anything you did, so log model changes beside your own.

No source in this research gives a reliable timeline, and anyone quoting a fixed number of days is guessing. Treat the first four weeks as the observation window, and judge by trend across repeat runs, not by any single answer.

## How do I re-test so I know the fix worked?

Use a fixed prompt set and compare like with like. This table is a template for the before and after record.

| Field | Before fix | After fix |
| --- | --- | --- |
| Prompts in set | Same list, version number recorded | Identical list |
| Runs per prompt per engine | 5 or more | Same count |
| Claim appearance rate | Runs containing the claim divided by total runs | Same formula |
| Cited source for the claim | URL list | URL list |
| Sentiment wording | Adjectives used near the brand | Adjectives used near the brand |
| Dates and model notes | Recorded | Recorded |

Say a fix worked only when the change is larger than the run-to-run variation you saw before the fix. The [AI Visibility Measurement Standard](https://nomahq.app/resources/ai-visibility-measurement) sets out the same discipline: state numerator and denominator, record settings and dates, and attach a margin of error. For the sampling logic, see [AI citation stability](https://nomahq.app/blog/ai-citation-stability).

### A worked example of the record

This is an illustration of the method, not a result. Suppose a prompt is run 10 times on one engine and the claim "support is slow" appears in 7 runs, citing one review page. After you update your support page with dated response-time figures and the reviewer updates their post, you run it 10 times again and the claim appears in 3. That is 70 per cent to 30 per cent. Run it a third time a week later: if it holds near 30, the change is probably real. If it bounces back to 60, you saw noise.

## What does not work?

- **Asking the engine to forget.** A chat correction does not rewrite the next user's answer.
- **Deleting or suppressing one page.** Other pages usually carry the same claim.
- **Publishing one defensive page and stopping.** Engines weigh consistency across sources. Your own page must agree with directories, profiles and press.
- **Judging by one prompt.** Different engines disagree for sound reasons, covered in [why engines disagree about your brand](https://nomahq.app/blog/why-engines-disagree).
- **Hiding real criticism.** Fix the cause, then publish evidence.

If the engine states something wrong about price or features, the escalation steps in [what to do when AI gets your brand wrong](https://nomahq.app/blog/when-ai-gets-your-brand-wrong) and the [correction runbook](https://nomahq.app/resources/ai-correction-runbook) cover feedback routes after the source fix.

## Where Noma fits

Noma by Nurdd asks 13 AI engines, including ChatGPT, Gemini, Perplexity and Claude, your buyers' questions every day. For each engine it reports visibility, position and sentiment, the wording that sits beside your brand name, and the sources each answer cites. Because the prompt set is fixed, a team can compare answers before and after a correction and see which cited pages still carry the negative claim.

See it on your own brand: [book a demo](https://nomahq.app/demo) or [start the 3-day free trial](https://platform.nomahq.app/signup). A card is taken at signup so billing can start on day 3; cancel before then and nothing is charged.

## A checklist to run this week

1. Choose 10 to 20 prompts where the negative framing appears; save answers and cited URLs.
2. Run each prompt at least five times per engine.
3. List each claim and its appearance rate.
4. Trace each claim to its source page.
5. Label each claim wrong, outdated, unframed or true.
6. Fix your own pages and check crawler access with the [AI SEO Checker](https://nomahq.app/tools/ai-seo-checker).
7. Send correction requests to third-party publishers.
8. Publish dated, quotable facts for each claim.
9. Re-run the identical set weekly for four weeks.

Teams that want help prioritising what to publish can use [gap analysis](https://nomahq.app/help/article/what-is-gap-analysis), and the [guide to getting cited more often](https://nomahq.app/blog/get-cited-more-often) covers the content side in more depth.

## How we researched this

We reviewed platform documentation from Google Search Central and OpenAI, a citation-stability study from Ahrefs, a report on Ahrefs' ChatGPT citation data, the published GEO research summary, and three current articles on repairing AI sentiment to see which steps they cover and what they leave out. Facts, figures and prices were checked on 2026-10-31. We did not run customer tests, and the worked example above is a labelled illustration.

## What to do next

Start with the baseline: save today's answers before you touch a page. Then trace, triage and fix. To track the same prompts across 13 AI engines and see the answers before and after your changes, [book a demo](https://nomahq.app/demo) or [start the 3-day free trial](https://platform.nomahq.app/signup).