Monitoring brand sentiment on AI platforms: the tools we use with clients
How we monitor brand sentiment on AI platforms for clients, what to score, how to trace a bad description to its source, and the tool we run it on.
Clients rarely ask "what is our sentiment score in ChatGPT." They ask something more specific: why does it describe us as expensive, or outdated, or leave us out of the shortlist? Sentiment monitoring on AI platforms is only useful if it can answer that.
What sentiment means here
Social listening scans posts. AI platforms generate an answer that blends many sources. So we monitor sentiment by asking a fixed set of questions and scoring each answer. The three kinds of prompts we use are brand prompts ("what do you know about X"), category prompts ("best tools for Y") and comparison prompts ("X vs Z"). Sentiment shifts most in comparisons, so we watch those closely.
A single score is a headline. The real value is reading the individual responses that pull the average down.
What we need from a tool
Per-response scoring. We want to open the worst answers and read them.
A weekly summary. Clients will not read 500 responses. A short written summary of what changed saves time.
Source tracing. If a model repeats an outdated claim, which page does it cite? That decides whether the fix is on our client's site or somewhere else.
Control over what counts as a mention. Brand names that are also common words create noise.
How Promptwatch handles it
We run client programs on Promptwatch. Sentiment is scored per response from 1 to 100 and summarized weekly by AI, next to Visibility Score, Share of Voice and Position in Answer. The Brand Book lets us write analysis instructions that define what counts as a mention, which has helped with ambiguous names.
The part we value most is what follows a poor score. Citation analytics shows the pages and domains cited for a prompt, along with Reddit, YouTube and LinkedIn reports and offsite mentions where the brand is named without a link. If a bad description traces to a forum thread or an old review page, we know where to work. If it traces to the client's own outdated page, content refresh and the Action Board handle it. Crawler logs let us check that AI bots can actually read the updated page.
Plans, as of late October 2026: Explore is free with 10 prompts on ChatGPT only. Essential is $95 a month, Professional $245 and Business $579. Agency plans run $199, $399 and $799 a month.
What other tools offer
AthenaHQ includes sentiment inside its GEO score and meters usage in credits. Scrunch AI includes sentiment analysis with audits and bot reports. Profound is an enterprise platform with answer-level analytics. Evertune samples each prompt many times per model, which suits research-style reporting, though its pricing is not public. Suite add-ons exist from HubSpot and Semrush. Each has a place. We prefer ones where the next step after the score is visible in the same workspace.
Our workflow
- Baseline sentiment per topic and per model.
- Read the lowest-scoring responses in full.
- Open citations for those prompts and note the sources.
- Decide: fix our page, refresh content, or address a third-party source.
- Re-check the same prompts after a few weeks.
What to tell stakeholders
Sentiment from AI is an estimate. It varies run to run and by country, so we report trends across weeks, not single scores, and we never promise that a particular wording will appear. What we do promise is that you will know where a description comes from and what can be done about it.
If sentiment is your concern, start by reading twenty real answers about your brand. The score helps you scale that reading. It does not replace it.