Sentiment Analysis

Sentiment analysis, as a metric, quantifies the emotional tone within text data. It moves beyond simply counting mentions—which could be negative—to provide a measurable value of how those mentions are positioned. They are typically categorized as positive, negative, or neutral. 

Brand sentiment within large language models carries particular significance because these systems increasingly influence consumer decision-making through AI-powered search results and shopping recommendations. 

When users ask LLMs like ChatGPT, Gemini, and Perplexity about products or services, the model's response can shape purchasing decisions and brand perception. Negative sentiment embedded in training data can perpetuate unfavorable associations, while positive sentiment reinforces brand strength and promotes purchases. 

This is why it's important to monitor brand sentiment in LLMs. Unlike traditional search, where users evaluate multiple sources, AI responses often present a single, synthesized viewpoint that may reflect the dominant sentiment in the model's training data. This makes proactive sentiment monitoring and reputation management within AI systems essential for maintaining competitive positioning in an AI-driven marketplace.

Many AI search monitoring tools assign numerical scores to reflect intensity and identify complex sentiment. This allows for quantifiable insights into public perception. You can monitor your brand sentiment in LLMs in Wix’s AI Visibility Overview dashboard.