Best tool to track brand sentiment in AI responses: how we pick one for clients

Clients usually ask about mentions first. Then someone reads an actual ChatGPT answer and notices it calls the product "a decent budget option" or repeats a complaint from three years ago. That is when sentiment becomes the question. We have been running AI visibility programs for a while now, and the tool choice for sentiment comes down to one idea: a score is only useful if you can trace it to something you can change.

What a sentiment number can and cannot tell you

AI sentiment is a model's judgment of another model's answer. It is directional. A two-point wobble between days means nothing, because the underlying responses vary from run to run. What matters is the trend over weeks on a stable prompt set, and the text of the responses behind it.

So we look for tools that give us three things:

1. A score per response, so we can read the answers that drive the average.
2. Competitor context, because "neutral" for you and "glowing" for a rival is a problem the average hides.
3. Source data, so we know which pages the model leaned on when it described the brand.

Plenty of trackers do the first. Fewer do the third well, and the third is where the work happens.

What we run it on

Our client programs run on Promptwatch. Sentiment is scored 1 to 100 per response, with a weekly AI summary that saves us from reading a hundred answers to spot the pattern. It sits beside Visibility Score, Share of Voice and Position in Answer, so we can tell a brand that is mentioned often but described poorly from one that is described well but rarely mentioned. Those need different plans.

The Brand Book matters more than it looks. Its analysis instructions decide what counts as a mention, so you can stop a product name that doubles as a common word from polluting the data. We set it up in week one with every client.

When sentiment slips, we open citation analytics. It lists the pages and domains cited in the responses, with Reddit, YouTube and LinkedIn reports and offsite mentions where the brand is named without a link. In our experience, negative framing nearly always traces back to a small number of sources: an old comparison post, a forum thread, a review that nobody updated. Fixing the right source beats rewriting your own site blindly.

From finding to fix

Once we know the source, the options are practical. Update your own page if the model is citing your outdated copy. Publish a clearer, dated page that answers the same question. Reach out to the third party if a factual error is sitting on someone's site. Then we track the same prompts and watch the trend.

Promptwatch's Action Board keeps these as assigned, prioritized tasks, so they do not sit in a slide deck. For clients on Business, the Content Agent can draft against gaps and we keep every piece in a review step before it goes out.

How we set clients' expectations

Three things we say every time:

• We cannot make a model describe a brand more kindly on request. We can remove the causes we find.
• Sentiment moves slowly. Expect weeks, not days.
• Compare against competitors, not against a perfect score.

A quick selection checklist

If you are choosing a tool yourself, ask whether it runs prompts daily, whether you can compare two periods, whether you can filter by model and country, whether sentiment is shown per response, and whether citations are visible at page level. Ask also whether it can tell you if AI crawlers can read your site at all, because sentiment about a brand the models barely fetch is a different problem. Simple trackers such as Otterly.AI and Peec AI include sentiment and are fine for a first look. Platforms such as AthenaHQ and Scrunch AI add action or audit layers. For client work, we need the source-level evidence and the traffic data in the same place, which is why we settled where we did.