Sentiment Analysis
In an AI-search context, sentiment analysis is the classification of how AI assistants describe a brand — positive, neutral, or negative — across the answers they generate, beyond just whether the brand is mentioned.
Why it matters
Citation frequency is necessary but not sufficient. A brand that's frequently mentioned but consistently described as 'expensive' or 'has poor support' is suffering an AEO problem that pure visibility metrics miss. Sentiment is the signal that catches it.
What it measures
For each AI-generated answer that mentions a brand, sentiment analysis classifies the framing of the mention:
- Positive — the brand is recommended, praised, or framed as the strongest option.
- Neutral — the brand is mentioned factually without an evaluative slant.
- Negative — the brand is described with caveats, complaints, or as inferior to alternatives.
Modern sentiment analysis goes further than positive/negative: it identifies recurring themes (pricing concerns, ease-of-use complaints, support criticism) so the brand can act on the underlying narrative.
How it's done
The classifier itself is usually an LLM (often the same model family being tracked, or a separate evaluator) that reads the AI-generated answer and labels each brand mention. Quality depends on a stable rubric, consistent prompts, and ideally human-labelled validation samples.
What to do with the data
- Track topic shifts — if 'expensive' starts appearing in mentions where it didn't before, something changed in the AI's grounding sources.
- Find narrative gaps — themes you'd want associated with your brand that don't appear in any AI mention.
- Spot misinformation — negative sentiment driven by inaccurate facts can be addressed at the source.
Related terms