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What is it?

Sentiment measures the emotional tone associated with your brand when LLMs mention it. It can be either positive, neutral, or negative. It answers: “When AI brings up my brand, how does it talk about me?”

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How is it calculated?

Sentiment is calculated by using machine-learning techniques to understand the context around how your brand is being mentioned. Additionally, we can calculate the overall sentiment percentage: Positive Sentiment % = Positive Mentions / Total Mentions

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Why Track It?

Because LLMs don’t just list brands, they explain them. The tone used to describe your brand affects trust and overall conversion. Negative or neutral sentiment may indicate:
  • Old controversy still influencing model memory
  • Poor online reviews feeding into training data
  • Outdated product messaging
  • Weak positioning compared to competitors
  • Missing positive proof (case studies, technical docs, testimonials)
Positive sentiment is a strong predictor of:
  • Higher recommendation rates
  • Stronger AI-driven conversions
  • Brand preference in ambiguous or “best tool for X” queries

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Real Use Cases

1. Detecting outdated information
A cybersecurity startup discovers ChatGPT repeatedly describes their 2022 outage as current. Sentiment alerts help them identify training-data residue they can counteract with updated content. 2. Competitor comparison
Sentiment shows that while a competitor appears slightly more often, the model speaks about them with neutral/negative tone due to pricing complaints. This is an advantage to exploit. 3. Brand health monitoring
Teams track whether major announcements (funding, new feature, partnership) generate a measurable shift toward positive sentiment in LLM answers.