How can I check whether AI assistants describe my brand positively or negatively?
Module
Bikma's Sentiment module measures the tone in which AI assistants describe the brand, broken down by question cluster and by model.
How it works in Bikma
Every answer that names the brand is classified by polarity and aggregated by cluster and by model. What emerges is not just an average tone, but where the tone is critical: on which topics, on which models, in which period. Problem queries surface before they turn into a commercial problem.
Mention and sentiment are different things
Bikma classifies tone as positive, neutral or negative, because being cited is not the same as being preferred. Appearing in an answer can mean very different things: being named as the best choice in the category, being named only to say that a competitor fits better, or being described as good «but not quite enough». These are situations with opposite implications that a plain mention does not distinguish, which is why Bikma measures mention and sentiment separately and ties sentiment to question clusters: that way a brand that is highly present but spoken of coolly or unfavourably shows up as a different problem from a brand that is barely present.
Why it matters
In AI, an inaccurate or negative description reaches every user who asks that question: monitoring sentiment means reacting at the source of the problem rather than to the single episode.