Created: Monday, June 30, 2025
What Is MINT Sentiment Analysis?
MINT sentiment analysis measures how AI models perceive and present your brand when users ask opinion-based questions. The system tracks positive, negative, and neutral mentions across multiple AI platforms to provide a comprehensive view of your brand reputation in AI-generated responses.
This analysis helps identify reputation risks, content opportunities, and sources that influence how AI systems represent your brand to users.
Video Tutorial: Sentiment Analysis Walkthrough
Watch our detailed explanation of sentiment analysis features, including score interpretation, heat map navigation, and source investigation techniques.
Understanding Your Sentiment Score
- Sentiment Score Calculation: Your sentiment score equals positive reviews minus negative reviews, divided by total responses. For example, if you receive 65 positive mentions with zero negative responses, your sentiment score is 65%.
- Score Tracking Over Time: Monitor sentiment trends to identify reputation changes, seasonal patterns, or the impact of marketing campaigns and product launches. Consistent tracking reveals long-term brand perception shifts.
- Model-Specific Analysis: Compare sentiment across different AI platforms (ChatGPT, Claude, Perplexity) to understand which models present your brand more favorably and identify platform-specific optimization opportunities.
Analyzing Sentiment Heat Maps
- Detailed Response Analysis: Click on sentiment heat maps to examine specific AI responses to questions like "What do customers say about [your brand]?" Each response provides granular insights into brand perception drivers.
- Positive Feedback Identification: Green highlights show top positive points and customer comments that AI models emphasize. Use these insights to reinforce successful brand messaging and identify content amplification opportunities.
- Areas for Improvement: Red highlights indicate potential improvement areas mentioned by AI models. Address these concerns through content optimization, product development, or targeted communication strategies.
- Cross-Model Comparison: Review responses across different AI models to understand varying brand perception patterns and identify consistent themes requiring attention.
Source Citation Analysis
- Web Search Tracking: Access the comprehensive database showing which websites AI models consulted when generating sentiment responses. This reveals the sources most influential in shaping your brand perception.
- Citation Pattern Understanding: Analyze which domains AI models reference most frequently for brand information. High-citation sources represent critical reputation management targets.
- Content Strategy Development: Use source data to identify partnership opportunities, content gaps, and influential platforms requiring attention or collaboration.
Source Watchtower Monitoring
- Harmful Source Detection: MINT automatically monitors sources that could damage your brand reputation. The system flags potentially problematic content that influences negative AI responses about your brand.
- Risk Assessment: Review risk levels and source counts to understand reputation threats. Zero harmful sources indicate low risk, while higher counts require immediate attention and mitigation strategies.
- Proactive Reputation Management: Use watchtower alerts to address reputation risks before they significantly impact AI-generated responses about your brand.
Detailed Source Investigation
- Comprehensive Source Review: Access complete lists of domains consulted by AI models, filtered by date ranges and specific AI platforms. This granular view reveals content influence patterns.
- Content Influence Assessment: Determine whether consulted sources provide positive, neutral, or negative sentiment contributions to AI responses. Focus optimization efforts on high-influence, negative-sentiment sources.
- Partnership Strategy Development: Identify positive-sentiment sources for potential content partnerships, guest posting opportunities, or relationship building initiatives.
Strategic Applications
- Content Optimization: Use sentiment insights to create content that addresses negative perceptions while amplifying positive brand attributes that AI models already recognize.
- Reputation Management: Monitor harmful sources and develop mitigation strategies for negative content that influences AI responses about your brand.
- Partnership Development: Build relationships with high-influence, positive-sentiment sources to strengthen your brand's representation in AI training data and responses.
- Competitive Intelligence: Compare your sentiment patterns with competitors to identify differentiation opportunities and benchmark reputation management effectiveness.
Best Practices for Sentiment Monitoring
- Regular Review Schedule: Check sentiment reports weekly to identify emerging reputation trends and address issues before they impact broader AI platform representation.
- Source Relationship Building: Engage with high-influence sources that provide positive sentiment to strengthen ongoing brand advocacy and content collaboration.
- Negative Feedback Response: Develop systematic approaches to address negative sentiment sources through improved customer service, product enhancements, or direct outreach.
- Content Strategy Alignment: Align content creation efforts with sentiment insights to reinforce positive perceptions and address areas needing improvement.
Interpreting Sentiment Trends
- Positive Trend Indicators: Increasing sentiment scores, growing positive source citations, and declining harmful source flags indicate effective reputation management.
- Warning Signs: Declining sentiment scores, increasing negative citations, or growing harmful source counts require immediate investigation and response strategies.
- Seasonal Patterns: Monitor sentiment fluctuations related to product launches, marketing campaigns, or industry events to understand external factors affecting brand perception.
Frequently Asked Questions
What does MINT sentiment analysis measure?
MINT sentiment analysis measures how AI models perceive and present your brand when users ask opinion-based questions, tracking positive, negative, and neutral mentions across multiple AI platforms.
How is my sentiment score calculated?
Your sentiment score equals positive reviews minus negative reviews, divided by total responses. For example, 65 positive mentions with zero negative responses creates a 65% sentiment score.
What do sentiment heat maps show me?
Sentiment heat maps display specific AI responses to brand questions, with green highlights showing positive points and red highlights indicating improvement areas mentioned by AI models.
Which sources do AI models consult for sentiment information?
The source citation analysis shows which websites AI models consulted when generating sentiment responses, revealing the most influential sources shaping your brand perception.
What is the Source Watchtower feature?
Source Watchtower automatically monitors sources that could damage your brand reputation, flagging potentially problematic content that influences negative AI responses about your brand.
How can I investigate which domains influence my sentiment?
Access complete lists of domains consulted by AI models, filtered by date ranges and AI platforms, to determine whether sources provide positive, neutral, or negative sentiment contributions.
What indicates positive sentiment trends?
Positive indicators include increasing sentiment scores over time, growing positive source citations, and declining harmful source flags showing effective reputation management.
What are warning signs in sentiment analysis?
Warning signs include declining sentiment scores, increasing negative citations, or growing harmful source counts that require immediate investigation and response strategies.
How often should I review sentiment reports?
Check sentiment reports weekly to identify emerging reputation trends and address issues before they impact broader AI platform representation of your brand.
This guide explains how to monitor and improve your brand sentiment in AI-generated responses. For advanced sentiment analysis techniques and reputation management strategies, contact our customer success team.