How is AI sentiment different from social media sentiment?

Social media sentiment reflects what people say about your brand in public conversations. AI sentiment reflects what AI engines say about your brand when users ask for recommendations. AI sentiment can be more impactful because it reaches users at the decision point, when they are actively choosing between options, and it can be more persistent because AI responses are generated from aggregated content signals rather than ephemeral social posts. A brand can have positive social media sentiment but negative AI sentiment if the content AI engines draw from frames the brand unfavourably.

Can I change how AI engines describe my brand?

Yes, but indirectly. AI engines generate descriptions based on their training data and retrieved content. By updating your website content, publishing new content that addresses specific sentiment themes, and building citations from authoritative sources with favourable framing, you can shift AI sentiment over time. The key is understanding which specific content sources drive the current framing, so search for the phrases engines use and find where they originate.

How quickly does AI sentiment respond to content changes?

For retrieval-based engines (Perplexity, AI Overviews), framing can shift within a few weeks of publishing new content. For engines that rely more on training data, changes are reflected more slowly. Shifts are usually gradual rather than sudden, as AI engines aggregate signals from multiple sources. Rerun the same evaluation prompts at regular intervals so you can see early signs of change.

Free tool, not actively developed; no accounts. AEO Platform no longer sells subscriptions.

Use case

AI Brand Sentiment Monitoring

Review how AI engines describe your brand's strengths, weaknesses, and positioning.

Quick answer

AI engines do not just mention brands; they frame them with descriptions of strengths, weaknesses, and positioning. This framing directly influences buyer perception, but most companies have no routine for checking how AI engines characterise their brand or noticing when the framing shifts unfavourably.

Expected outcomes

What you can achieve

Baseline

of how engines frame your brand

Themes

grouped from the phrases engines use

Source trace

from framing to likely content origins

Monthly

re-review to compare themes

How it works

How to approach ai brand sentiment monitoring

Establish a sentiment baseline

Run evaluation prompts in each engine and copy the descriptive phrases used for your brand: strengths, weaknesses, and common framing.

Compare against competitors

Note how competitors in the same responses are described. Identify framing advantages and disadvantages.

Group phrases into themes

Cluster the phrases into themes (for example pricing perception, ease of use, enterprise readiness) so you can address specific narrative areas.

Trace themes to sources

Search for each phrase on your own site, review sites, and third-party articles. Work out whether negative framing comes from your content, competitor content, or other sources.

Take corrective action

Update content that reinforces negative framing, publish content that supports the positive themes, and build citations from sources with a favourable perspective.

The solution

How to approach it

A manual sentiment review works well for this use case because the signal is in the wording of answers. Run a set of evaluation prompts, such as "what are the pros and cons of [your brand]?", "is [your brand] worth the price?", and "is [your brand] good for enterprise?", in each major engine.

For each answer, copy the descriptive phrases into a sheet. Tag each phrase as positive, neutral, or negative, and group phrases into themes such as pricing perception, ease of use, support, and enterprise readiness. Do the same for two or three competitors that appear in the same answers, so you can compare framing.

Then trace each theme to likely sources. Search for the phrase on your own site, on review sites, in forum threads, and in comparison articles. Often the framing comes from a specific page that is out of date or from a third-party source. Update your own content, publish clear material that addresses the theme, and work on third-party sources where you can.

Repeat the review monthly and compare themes between rounds. Generic AI visibility monitoring tools can automate the collection if you need scale. For the technical side, the free AEO Platform AI-readiness check confirms whether AI crawlers can reach the corrected pages. It does not score sentiment or read live AI answers.

Example queries

Queries to monitor

“What do people think about [your brand]?”

“Is [your brand] worth the price?”

“What are the downsides of [your brand]?”

“How does [your brand] compare for ease of use?”

“[Your brand] pros and cons”

“Is [your brand] good for enterprise?”

FAQ