{
  "slug": "ai-positive-mention-count",
  "name": "Positive AI mentions",
  "aliases": [
    "Positive Mentions",
    "Total positive mentions"
  ],
  "definition": "The number of answer-engine responses that named your brand and were classified positive in tone.",
  "category": "AI visibility",
  "type": "raw measure",
  "unit": "mentions classified positive",
  "direction": "higher-better",
  "verification": "verified",
  "calculation": "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.",
  "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.",
  "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"
  ],
  "requiredFilters": [
    "tracker (unified segment id) or tracked segment id",
    "date range"
  ],
  "sources": [
    "ai-visibility"
  ],
  "reports": [],
  "skills": [
    "aeo-audit",
    "competitor-deep-dive",
    "quattr-iq"
  ],
  "verbs": [
    "ai_visibility_overview",
    "ai_visibility_competitors",
    "ai_visibility_drilldown"
  ],
  "rungs": [
    "R4"
  ],
  "levers": [
    "L7"
  ],
  "questions": [
    "How many AI answers described us positively?",
    "Did positive mentions grow with total mentions or faster?"
  ],
  "interpretation": "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": [
    "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.",
  "failureModes": [
    "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."
  ],
  "notSameAs": [
    {
      "slug": "ai-positive-sentiment-rate",
      "why": "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": [
    "ai-positive-sentiment-rate",
    "ai-mention-count",
    "ai-neutral-sentiment-rate",
    "ai-negative-sentiment-rate"
  ],
  "workflows": [],
  "lastVerified": "2026-08-04"
}