How does the Sentiment Analysis work?

Understand how AI platforms describe your brand and identify opportunities to improve brand perception and positioning.

Written By Tom van den Heuvel

Last updated 6 months ago

Understanding AI Sentiment Analysis

Sentiment analysis reveals how AI platforms characterize your brand when mentioning it to users, providing insights into brand perception and positioning opportunities.

Sentiment Categories

Positive Sentiment:

  • Recommended, excellent, high-quality language

  • Emphasis on benefits and advantages

  • Positive customer experience references

  • Authority and trust indicators

Neutral Sentiment:

  • Factual mentions without emotional language

  • Basic product or service descriptions

  • List inclusions without commentary

  • Objective comparison references

Negative Sentiment:

  • Critical language about issues or problems

  • Comparison disadvantages

  • Customer complaint references

  • Warning or caution language

Sentiment Tracking Dashboard

Overall Brand Sentiment

The main sentiment indicator shows:

  • Primary sentiment classification (Positive, Neutral, Negative)

  • Sentiment distribution percentage across all mentions

  • Trend analysis showing sentiment changes over time

  • Platform-specific sentiment breakdown

Sentiment by AI Platform

ChatGPT Sentiment:

  • How ChatGPT typically describes your brand

  • Common language patterns and descriptors

  • Context of positive vs. neutral mentions

  • Frequency of recommendations vs. basic mentions

Claude Sentiment:

  • Claude's characterization of your brand

  • Citation patterns and source references

  • Detail level and context provided

  • Comparison language and positioning

Google Gemini Sentiment:

  • Gemini's brand descriptions and recommendations

  • Integration with search and shopping results

  • Knowledge panel sentiment and information

  • Product recommendation language

Sentiment Analysis Insights

Positive Sentiment Indicators

Recommendation Language:

  • "Highly recommended," "excellent choice," "top-rated"

  • "Popular," "well-regarded," "trusted brand"

  • "High-quality," "premium," "professional-grade"

Benefit-focused Descriptions:

  • Specific advantages and unique features

  • Customer satisfaction and success stories

  • Performance and quality highlights

  • Value proposition emphasis

Authority Signals:

  • Industry recognition and awards

  • Expert endorsements and certifications

  • Market leadership positioning

  • Innovation and expertise references

Neutral Sentiment Patterns

Factual Mentions:

  • Basic product availability and specifications

  • Simple category or list inclusions

  • Objective feature descriptions

  • Standard comparison mentions

Improvement Opportunities:

  • Add more specific benefits and differentiators

  • Include customer testimonials and reviews

  • Highlight unique value propositions

  • Build authority through certifications

Negative Sentiment Analysis

Common Negative Themes:

  • Price or value concerns

  • Product limitations or drawbacks

  • Customer service issues

  • Comparison disadvantages

Addressing Negative Sentiment:

  1. Identify specific issues mentioned

  2. Update content to address concerns

  3. Highlight improvements or solutions

  4. Build positive counter-narratives

Improving Brand Sentiment

Content Optimization for Positive Sentiment

Highlight Unique Benefits:

  • Specific advantages over alternatives

  • Measurable improvements and outcomes

  • Customer success stories and testimonials

  • Professional endorsements and certifications

Build Authority and Trust:

  • Include credentials, awards, and recognition

  • Showcase expertise and industry experience

  • Display customer reviews and ratings

  • Highlight guarantees and quality assurance

Address Common Concerns:

  • Proactively address potential objections

  • Provide detailed information about limitations

  • Offer solutions and alternatives

  • Demonstrate commitment to customer satisfaction

Best Practices for Sentiment Management

Proactive Sentiment Building

Create Positive Content:

  1. Develop customer success stories and case studies

  2. Showcase awards, certifications, and recognition

  3. Build comprehensive FAQ sections addressing concerns

  4. Create educational content demonstrating expertise

Monitor and Respond:

  1. Regular sentiment tracking and analysis

  2. Quick response to negative sentiment drivers

  3. Continuous content improvement and optimization

  4. Building long-term positive brand narrative

Avoiding Sentiment Pitfalls

Don't Over-Promise:

  • Make realistic claims about product benefits

  • Include honest limitations and considerations

  • Avoid superlative language without backing

  • Focus on specific, measurable advantages

Maintain Authenticity:

  • Use genuine customer testimonials and reviews

  • Present balanced information about products

  • Address negative feedback constructively

  • Build trust through transparency