AI Sentiment Monitoring

Why It Matters

An AI system that mentions your brand in a negative context-even once-can influence millions of users. Unlike social media sentiment, AI sentiment is often presented as objective fact, making negative AI sentiment particularly damaging. Proactive monitoring enables rapid identification and remediation of negative AI brand narratives.

How It Works

AI sentiment monitoring tools query AI platforms with brand-relevant prompts, extract brand mentions from responses, and apply NLP sentiment classification to assess tone. Results are aggregated over time and across platforms to identify sentiment patterns and flag anomalies for investigation.

Use Cases

• Detecting that an AI platform consistently describes a brand's customer service as poor
• Identifying sentiment shifts following a product recall or PR crisis
• Comparing sentiment across AI platforms to find platform-specific reputation issues
• Tracking how new content publication affects AI sentiment over time
• Monitoring competitor sentiment to identify positioning opportunities

Best Practices

• Establish a sentiment baseline before launching major campaigns or content initiatives
• Monitor sentiment across multiple AI platforms-sentiment can vary significantly by platform
• Correlate sentiment changes with content publication, PR, and product events
• Publish authoritative, positive content to counteract negative AI sentiment signals
• Investigate negative sentiment context to identify root cause content or references
• Track sentiment at both brand and product/feature level for granular insight