AI brand sentiment describes the tone of an answer about a brand. It is different from visibility, a recommendation and customer satisfaction. Review the relevant passage before converting a label into a reputational conclusion. This procedure focuses on contextual interpretation rather than selecting software.
Define what the label applies to
Specify whether you are classifying the sentence about the brand, the whole answer or a comparison. An answer can praise a feature and warn that the product is unsuitable for a particular buyer. A single positive or negative label can hide that distinction. Document the unit of interpretation and use it consistently.
Use a review rubric
| Passage type | Interpretation | Follow-up |
|---|---|---|
| A factual limitation | May be neutral rather than negative | Verify the product fact |
| A preference for a competitor | Comparative suitability | Check the buyer's stated requirements |
| An unsupported critical claim | Potential accuracy issue | Inspect cited evidence and official facts |
| A positive statement without evidence | Positive wording, weak support | Avoid treating it as proof of customer trust |
The rubric is a proposed review method. It does not describe company measurements or a tested classifier.
Preserve the question and context
Keep the question, model or system, date, market and relevant passage together in an internal review record. Separate product categories and languages where context differs. Do not average incompatible scopes and then call the result a universal reputation score. A tone label produced by an automated system should be treated as a classification requiring interpretation, not a fact about how all customers feel.
Decide whether content needs changing
If the answer repeats an accurate limitation, clarify the appropriate use case on the existing product page. If it states an obsolete feature, check your official documentation and public third-party descriptions for inconsistency. If it provides an unsupported claim, avoid publishing that claim as your own finding. Record the issue privately and prepare a factual correction using approved public information.
In a fictional example, a software tool is described as unsuitable because it lacks a particular integration. The content task is to explain the actual supported integration or the real limitation. It is not to add vague positive adjectives to the homepage.
Where Semly fits
Semly's public documentation includes sentiment alongside visibility, position and sources. These dimensions answer different questions and should remain distinct. A brand can be frequently mentioned in a context that is not a buying recommendation. Conversely, a favourable passage may be rare in the observed scope.
The completion condition is that the relevant claim, evidence and corrective action are understandable. Do not promise that editing one page will change a model's tone or guarantee a favourable reputation.
Sources and preparation
AI assisted with organizing this procedure. References were checked against public documentation on 3 October 2026. This is implementation guidance, not a company-results report or a test of ranking impact.
