AI Sentiment Analysis: Understanding How LLMs Describe Your Brand

AI sentiment analysis examines the tone, framing, and characterization that large language models use when discussing your brand. A brand might be mentioned frequently but described in neutral or negative terms — sentiment analysis reveals these qualitative nuances.

What Is AI Sentiment Analysis?

When an AI model responds to "What are the best project management tools?", it might say "Asana is popular and well-designed" (positive) or "Asana is functional but has a steep learning curve" (mixed). AI sentiment analysis systematically categorizes these characterizations across many prompts and models.

Why AI Sentiment Matters

A mention is not automatically beneficial. If AI models consistently pair your brand with negative qualifiers, those mentions may actively harm perception. Conversely, consistent positive sentiment amplifies each mention's value.

How to Measure AI Sentiment

Citerna analyzes sentiment across 11 models, categorizing responses as positive, neutral, or negative, and extracting specific language used. This identifies patterns and tracks changes over time.

Improving AI Sentiment

Address root causes of negative sentiment. Publish positive case studies and customer success stories. Provide AI models with positive material to draw from.

Competitive Sentiment Analysis

Compare your AI sentiment against competitors. If competitors receive consistently more positive sentiment, analyze what content or public perception data might be influencing the AI.