How conservative entity resolution and explicit position rules turn natural-language recommendations into reviewable signals.
Mention extraction requires a brand registry
Official names, abbreviations, product brands, parents, alternate spellings, domains, and regional names may refer to the same entity. Store canonical brands and conservative aliases while preserving exact, alias, and indirect mention types.
Alias expansion must respect context
“IBM” can safely map to International Business Machines in most business contexts; “Apple” may describe a company or fruit. Entity resolution should retain evidence spans and confidence rather than force every surface form into a brand.
Define position before seeing the results
Ranked lists allow ordinal extraction, but narrative answers may support first occurrence, recommendation order, section order, or judged prominence. Choose a consistent rule in advance and report position separately from mention rate.
Sentiment needs more observations and reviewable spans
Kumar found sentiment about 6.7 times noisier than mention at the same observational level. Store positive, neutral, or negative classification alongside exact text and use more observations before drawing conclusions.
Do not turn one qualified sentence into a permanent negative-brand label.
Questions about this topic
What belongs in a brand registry?+
Canonical names, domains, verified aliases, product and parent relationships, and regional naming variants.
Why is entity resolution difficult?+
Aliases can be ambiguous, products can differ from parents, and indirect references require contextual interpretation.
How should position be measured in narrative answers?+
Choose and document a consistent rule such as first occurrence or recommendation order before analysis.
Why does sentiment need more observations?+
It is materially noisier and more sensitive to wording and classification than simple mention presence.
References
Sources are listed in APA 7 style. Preprints are identified as such and should not be treated as peer-reviewed findings unless separately published.
- Kumar, P. (2026). Generative engine optimization at scale: Measuring brand visibility across AI search engines [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2606.20065
- Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14035