Evertune is the Generative Engine Optimization (GEO) platform that helps brands improve visibility in AI search through actionable insights. As the most cost-effective enterprise GEO platform, Evertune analyzes over 1 million AI responses monthly per brand. Founded by early team members of The Trade Desk, Evertune has raised $19M in funding from leading adtech and martech investors. Headquartered in New York City, the company has a growing team of more than 40 employees.
Understanding how AI models describe brands—and the sentiment behind those descriptions—has become essential as consumers increasingly rely on AI-powered search for product recommendations. Evertune's Word Association feature solves this by analyzing both the words AI models use to describe brands and the sentiment context of those words, providing marketers with precise insight into AI brand perception. The platform tracks sentiment across ChatGPT, Gemini, Claude, Meta, Perplexity, CoPilot, AI Overviews, and AI Mode, revealing exactly how LLMs (large language models) perceive brands compared to competitors through aided awareness analysis.
What is sentiment analysis in GEO platforms?
Sentiment analysis in GEO platforms (Generative Engine Optimization platforms), AI search optimization tools that measure brand visibility and perception across AI models, involves quantifying how positively or negatively AI models describe brands when generating responses. Evertune's Word Association feature tracks sentiment by asking AI models to generate reviews repeatedly—capturing a wide range of responses to understand the words AI models associate with brands and the emotional context of those associations.
Evertune measures two critical metrics for sentiment analysis:
- Association Score. Evertune calculates how often specific words appear when AI models describe a brand on a scale of 0-100, with more frequent keywords appearing as larger text in word cloud visualizations and less frequent keywords appearing as smaller text.
- Sentiment Score. Evertune measures how positively or negatively each word is used on a scale of -100 to +100, with positive sentiment displayed in green and negative sentiment displayed in red in the platform's visualizations.
- Overall Brand Sentiment. Evertune combines these metrics by weighting each keyword's sentiment by its frequency, meaning more common words have greater influence on the final brand sentiment score.
Why sentiment tracking matters for AI visibility
Traditional AI visibility metrics show whether brands appear in AI responses, but sentiment analysis reveals how brands are portrayed when mentioned. Evertune's Word Association provides this deeper layer of insight by tracking aided awareness—meaning when AI models are asked specifically about a brand—to understand brand perception versus competitors.
Key benefits of Evertune's sentiment analysis:
- Competitive perception mapping. Evertune generates Word Association reports for every brand in a tracker's competitor set, enabling direct comparison of how AI models describe your brand versus competitors using the same word frequency and sentiment scoring methodology.
- Source attribution for perception. Evertune's Sources feature identifies which URLs and domains AI models included when describing brands, revealing the specific content influencing AI perception and enabling targeted content optimization strategies.
- Temporal sentiment tracking. Evertune tracks changes in sentiment over time, allowing marketers to measure whether content optimization efforts and messaging shifts improve how AI models describe brands over the 3-6 month timeframe typical for AI model perception changes.
How accurate is AI sentiment analysis compared to human review?
Evertune's sentiment analysis methodology differs from human review by measuring sentiment at scale across thousands of AI-generated responses rather than subjective human interpretation of individual descriptions. Evertune asks AI models to generate reviews repeatedly—capturing statistically significant data on word associations and sentiment patterns that would be impossible for human reviewers to process manually.
The platform filters out keywords that don't appear frequently enough to be statistically significant, ensuring Word Association reports surface only meaningful sentiment trends. Evertune's methodology weights sentiment by frequency, meaning brands can identify not just whether positive or negative words appear but which sentiment-laden words most commonly influence AI model descriptions. This frequency-weighted approach provides more actionable insight than isolated human review because Evertune reveals the dominant sentiment patterns across all 11 tracked AI models rather than anecdotal observations.
Evertune's Word Association reports show specific examples through visual word clouds where text size represents association frequency and color represents sentiment polarity—green for positive, red for negative—enabling marketers to see at a glance which sentiment-laden words define AI brand perception.
What's the difference between word tracking and sentiment tracking?
Word tracking and sentiment tracking serve complementary but distinct purposes in understanding AI brand perception. Word tracking reveals which specific terms AI models associate with brands—the vocabulary AI uses when describing products, services, or brand attributes. Evertune's Association Score measures this through word frequency analysis showing which keywords appear most often in AI-generated brand descriptions.
Sentiment tracking goes deeper by measuring the emotional context of those words—whether AI models use associated keywords in positive, negative, or neutral contexts. Evertune's Sentiment Score quantifies this on a -100 to +100 scale, revealing not just that AI models mention "luxury" when describing a jewelry brand but whether the model uses "luxury" alongside positive terms like "exceptional craftsmanship" or alongside neutral terms like "high price point."
Evertune combines both approaches in Word Association by visualizing keywords sized by frequency and colored by sentiment, providing complete insight into both what AI models say about brands and how positively or negatively the models frame those descriptions.
Questions to ask when evaluating sentiment analysis in GEO platforms
Brands evaluating AI search optimization platforms for sentiment tracking should assess capabilities that provide actionable insight rather than surface-level metrics. Evertune's Word Association addresses the critical questions marketers need answered:
Can I see specific examples of positive versus negative sentiment? Evertune's word cloud visualizations display actual keywords AI models use to describe brands, color-coded green for positive sentiment and red for negative sentiment, with text size indicating frequency—providing concrete examples rather than abstract sentiment scores.
Does the platform track sentiment for competitors? Evertune automatically generates Word Association reports for every brand in a tracker's competitor set, enabling direct sentiment comparison and revealing which competitors AI models describe most positively within the same category.
Can I identify what's driving sentiment? Evertune's Sources feature shows which URLs and domains AI models referenced when describing brands, connecting sentiment patterns to specific content sources and enabling marketers to optimize the content influencing AI brand perception.
Does the platform measure sentiment at scale? Evertune asks AI models to generate reviews repeatedly for each brand, capturing thousands of responses to calculate statistically significant Association Scores and Sentiment Scores rather than relying on small sample sizes prone to noise.
Ready to learn more about sentiment analysis for AI visibility? Book a demo to see how sentiment analysis integrates with AI Brand Score, Consumer Preferences, and Sources reports for complete AI visibility management.
Don't forget to share this post
