Positive AI mentions
also called Positive MentionsTotal positive mentions
Definition
The number of answer-engine responses that named your brand and were classified positive in tone.
How it's calculated
The sum of your brand's positive-classified mention counts across the answer-engine responses in the slice. It is the numerator of the positive sentiment rate, which the verbs read as its own measure rather than deriving it from this count.
Scope, grain and dimensions
- Grain
- One answer engine, prompt or competitor domain for one tracked segment over one date range.
- Dimensions
- answer engine · prompt · competitor domain · intent · branded vs non-branded
- Required filters
- tracker (unified segment id) or tracked segment id · date range
- Aggregation
- Additive across prompts and answer engines. It is the numerator of the positive sentiment rate, so it is reported beside its denominator rather than alone.
- Metric type
- raw measure · mentions classified positive
Data sources
Skills and analyses that use it
Skills carry the judgment; the analysis verbs do the reading.
Analysis verbs
ai_visibility_overviewai_visibility_competitorsai_visibility_drilldown
Method rungs and levers
A rung tells you what a movement here can and cannot explain, read the rungs below it first.
levers L7 AI-answer visibility
Ask Quattr
- "How many AI answers described us positively?" Simulate this →
- "Did positive mentions grow with total mentions or faster?" Simulate this →
How to read it
A count of positive mentions rises with mention volume, so it is read against the total mention count, the rate is the comparable figure.
Caveats, freshness and failure modes
Canon flags the Represented rung as only partly instrumented: mentions and sentiment are measured, the positioning-fidelity half is not.
Neutral and negative mention counts are not returned by these verbs, the competitor breakdown returns them as empty, so positive mentions cannot be differenced into the rest of the distribution.
The classifier has a neutral bucket, so total mentions minus positive mentions is not negative mentions.
Every figure is scoped to a tracked prompt basket, a sample, not a census, so the basket's coverage belongs beside the number it produced.
- Freshness
- ~1 day behind, Daily prompt runs, available the following day.
Common failure modes
- Inferring the negative count by subtracting positives from total mentions, the classifier's neutral bucket sits in between and is not returned here.
- Reading a rise in positive mentions as improved framing when total mentions rose by the same proportion.
Not the same as
The confusions that cause the most wrong decisions.
Positive AI mentions Positive sentiment share of AI mentions Compare definitions →
The count is the numerator; the rate divides it by all mentions. The rate is its own measure in the semantic layer and is never derived by the verbs from the count.
Related metrics and workflows
Verification
A definition is the smallest part of this.
The measurement matters because something acts on it. Here is the rest of the showcase, in the order most people find useful.