What tools track sentiment about my brand in ChatGPT answers?
Which tools track sentiment about your brand in ChatGPT answers? We compare AI visibility platforms on sentiment scoring, sources and fixes, with sourced pricing.
When ChatGPT describes your brand, the tone matters as much as the mention. "A solid budget option with limited support" and "the leading choice for growing teams" both count as a mention, and they do very different things to a buyer's shortlist. Sentiment tracking tries to measure that tone at scale.
A few tools do it well. Several do it loosely. This guide explains what sentiment tracking in AI answers actually measures, where it goes wrong, and which tools to look at.
What sentiment tracking measures
Sentiment tracking reads the AI's answer text, scores how the brand is described, and aggregates across many prompts and runs. Useful versions share four traits.
- Per-response scoring. Each answer gets a score, so you can drill into the worst ones.
- Aggregation over time. One answer is noise. A trend across hundreds of answers is signal.
- A link to sources. If the tone is negative, you need to see which cited page shaped it.
- A definition of what counts as your brand. Aliases, product names and misspellings need handling.
Sentiment tells you how you are described. It does not tell you why, and it cannot make the engine change its mind. For that you need citation data and a plan to influence the sources.
1. Promptwatch
Promptwatch scores sentiment per response on a 1 to 100 scale and adds a weekly AI summary. It sits next to Visibility Score, Share of Voice and position in the answer, and you can slice it by model, country, language, topic and prompt type. A Brand Book lets you set analysis instructions that decide what counts as a mention, and brand aliases handle naming variants. A/B period comparison shows whether sentiment moved after a change.
The follow-up work is where it separates itself. Citation analytics show which pages and domains the engine used, including Reddit, YouTube and LinkedIn reports and offsite mentions. If sentiment is poor, you can see the source shaping it. The Action Board then suggests prioritized actions, and the content tools can draft or refresh pages. Visitor analytics show whether AI-referred traffic converts, which puts sentiment in a business context.
ChatGPT tracking is covered by real-UI monitoring of ChatGPT Search. Pricing: a free Explore tier (ChatGPT only, 10 prompts), Essential $95 a month, Professional $245, Business $579.
2. AthenaHQ
AthenaHQ offers a unified GEO score with sentiment analysis across its tracked engines and an Action Center that turns issues into tasks. Free tier, then Starter at $295 a month. Our review notes credit-based usage that can run down quickly, and no Reddit monitoring or crawler analytics.
3. Scrunch AI
Scrunch AI includes sentiment analysis, site audits and insights alongside tracking, from $250 a month on Core. Its Agent Experience Platform serves crawler-optimized pages. Our review notes weekly data refreshes and vague optimization advice.
4. Brandlight
Brandlight tracks mentions, sentiment and citations for enterprise marketing teams, with a service layer of AI strategists. Pricing is not published, and our catalog lists estimates of $4K to $15K a month for enterprise, which are unverified. It measures but does not execute, per our review.
5. Profound
Profound offers answer-level analytics within an enterprise marketing-agent platform. The real product is an Enterprise contract at around $40k a year per our review, so it is not the quick answer to a sentiment question.
Monitoring-first options
Otterly.AI includes sentiment, from $29 a month, though our review notes it has been caught classifying neutral mentions as positive and data can be up to seven days stale. Peec AI tracks sentiment per prompt from $95 a month with three models included. Both are monitoring only.
Comparison
| Tool | Sentiment | Shows which sources shaped it | Fixes and content | Entry price per catalog |
|---|---|---|---|---|
| Promptwatch | Per-response 1 to 100 score, weekly AI summary | Page, domain, Reddit, YouTube, offsite | Action Board, content gap, Content Agent | Free; from $95/mo |
| AthenaHQ | Within GEO score | Yes | Action Center | Free; from $295/mo |
| Scrunch AI | Yes | Yes | Insights, page serving | From $250/mo |
| Brandlight | Yes | Yes | Measures, does not execute | Not published |
| Profound | Answer-level analytics | Citation share | Marketing agents | Enterprise |
How to read sentiment honestly
Run the same prompts repeatedly and look at trends, not single answers. Sentiment varies between runs and between models. Check a sample of scored answers by hand, since automated scoring can misread tone. And separate brand prompts ("is X any good") from category prompts, because the tone of each differs.
What to do when sentiment is poor
- Find the cited sources behind the negative answers.
- Decide whether the claim is wrong, outdated or fair.
- Fix your own pages, then address third-party sources where possible.
- Re-measure the same prompts.
No tool can force an engine to change how it describes you. You can improve the sources it reads and track whether the tone shifts.
Who should pick which
Pick Promptwatch if you want sentiment tied to the sources and a path to fix them. Pick AthenaHQ if you want a task queue. Pick Brandlight only if you are an enterprise team comfortable with an unpublished price. Start with Promptwatch and a brand prompt set.