In generative search, being mentioned is not enough. LLMs synthesise multiple sources into a single answer, which can result in mixed narratives — a brand may be praised by one source and criticised by another, sometimes within the same prompt. This effect is especially important at lower-funnel queries, where tone directly influences trust and decision-making. The Sentiment Score captures how a brand is portrayed within:Documentation Index
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- LLM-generated answers,
- the sources used to construct those answers.
-1 to +1, reflecting how positively or negatively the brand is expressed. The score also incorporates the intensity of the sentiment — stronger opinions weigh more than neutral or marginal mentions.
How the score is calculated
The score is built from individual brand mentions. For each prompt:- mentions detected in sources and answers are analysed individually,
- each mention receives a sentiment score from
-1to+1using a standardised NLP model (Google Natural Language Processing where supported, with fallback methods otherwise), - the score reflects both direction (positive, neutral, negative) and intensity (magnitude weighting strongly expressed opinions more heavily).
-1 to +1:
- negative sentiment is subtracted from positive sentiment,
- neutral mentions contribute to a balanced score around zero.
How to read the score
- A positive score suggests the brand is generally framed favourably.
- A neutral score often reflects factual or descriptive mentions.
- A negative score indicates recurring critical or unfavourable framing.
How this KPI fits with other signals
Read alongside Presence Rate and Visibility Score to understand not only where a brand appears, but how prominently and how it is framed in generative answers.What’s next
Sources & Links
See where mentions originate from.
Fan-out Queries
Understand the internal queries behind the answer.