The overall tone and favorability with which large language models describe and discuss your brand.
LLM brand sentiment measures the overall positivity, neutrality, or negativity of how large language models describe your brand when generating responses. This goes beyond simple mention counting to analyze the qualitative tone of AI-generated brand descriptions—whether LLMs characterize your brand favorably, present it as a strong option, note concerns or limitations, or position it unfavorably against competitors. LLM brand sentiment is shaped by training data sources, review profiles, media coverage, and the overall online narrative around your brand.
We monitor LLM brand sentiment across all major AI platforms and implement strategies to shift the narrative positively through better content, reviews, and PR.
Even when AI mentions your brand, the sentiment matters enormously. A recommendation with reservations ('Company X is popular but has mixed reviews on customer service') is far less valuable than an enthusiastic endorsement. Understanding and improving LLM sentiment is key to converting AI visibility into business results.
ChatGPT describing your product as 'a leading solution known for reliability' (positive sentiment)
Claude mentioning your brand but noting 'some users report a steep learning curve' (mixed sentiment)
Perplexity citing a negative review when summarizing your product's reputation (negative sentiment)
LLM sentiment cannot be directly manipulated through your own website content alone. AI models form sentiment from the aggregate of all sources—especially third-party reviews, media coverage, and user-generated content.
By querying AI models with brand-related prompts and analyzing the language, tone, and qualifiers used in responses. Sentiment analysis tools score responses as positive, neutral, or negative with specific themes identified.
Negative reviews on major platforms, unfavorable media coverage, unresolved customer complaints visible online, and outdated negative information that remains in training data. All of these shape how LLMs discuss your brand.
Improvements in retrieval-based systems (Perplexity) can appear within weeks as you address review and content issues. For base LLMs, sentiment changes require new training cycles, typically months.
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