Visibility tells you how often your brand is showing up in AI search. Sentiment tells you how AI is actually talking about you when it does. The Competitor Sentiment card tracks the overall tone of LLM responses across your brand and all of your tracked competitors, aggregated across every LLM Parsnipp monitors.
This is one of the more nuanced datasets in your Parsnipp account, and one the team is actively developing. A high visibility score is a good sign, but if the language AI uses when mentioning your brand is lukewarm, hedged, or positions you unfavourably relative to competitors, that matters just as much as whether you show up at all. Positive sentiment in AI responses tends to show up as enthusiastic recommendations, prominent positioning, and confident descriptions of your strengths. Negative sentiment is rarely blunt criticism. It more commonly appears as subtle qualifications, comparisons that favour rivals, or language that steers shoppers elsewhere without explicitly saying so.
One thing worth knowing upfront: LLMs tend to follow something close to a classroom rule of "say something nice or say nothing at all." In practice this means AI platforms skew towards neutral or positive framing even when there are genuine negatives, which can make it harder to surface the things that are actually working against your brand in AI conversations.
Parsnipp is actively working on ways to better illuminate these subtleties and help you identify and act on the areas where AI sentiment around your brand is less favourable than it could be. For now, the Competitor Sentiment card gives you an accurate read of the overall sentiment levels coming out of Parsnipp's simulated shopper conversations across all tracked LLMs, for your brand and every competitor you are monitoring.
Useful reading on the subject from others:
Brand Sentiment Analysis in AI: Complete Guide 2026 - A thorough breakdown of how AI sentiment actually works, why negative sentiment in LLM responses rarely looks like obvious criticism, and what the subtle linguistic patterns that favour or disadvantage a brand in AI responses actually look like in practice. Read it here
Brand Sentiment Analysis for AI Visibility in 2026 - Covers how LLMs form sentiment about brands based on their training data, why a brand's AI sentiment and its social media sentiment can differ significantly, and what marketers can do to shift the needle over time. Read it here
Brand Sentiment Analysis in LLMs: Complete Guide 2026 - Explains the dual-layer nature of LLM brand sentiment, why competitive benchmarking is essential for putting your own sentiment data in context, and how to build a baseline for tracking sentiment improvement over time. Read it here