What is Sentiment?
Sentiment measures the tone of how AI assistants describe your brand when they mention it. Being mentioned is good, but being mentioned positively is better. When ChatGPT recommends your product, does it say “highly recommended” or “has some limitations”? Sentiment analysis tells you.How Sentiment is Analyzed
We analyze the language surrounding each brand mention and classify it into three categories:Positive Sentiment
Language that indicates enthusiasm, recommendation, or praise:- “I highly recommend…”
- “Excellent choice for…”
- “Known for its outstanding…”
- “Best-in-class…”
- “Users love…”
Neutral Sentiment
Factual mentions without strong opinion:- “One option is…”
- “Available products include…”
- “Offers features such as…”
- “Can be used for…”
Negative Sentiment
Language indicating criticism, warnings, or unfavorable comparisons:- “However, some users report…”
- “A weakness is…”
- “More expensive than…”
- “Limited in terms of…”
- “May not be ideal for…”
Sentiment Dashboard
Overall Sentiment
Your sentiment breakdown shows the percentage of mentions in each category:- Positive: Percentage of mentions with positive sentiment
- Neutral: Percentage of factual/neutral mentions
- Negative: Percentage of mentions with negative sentiment
Sentiment Score
We calculate an overall sentiment score: Sentiment Score = ((Positive - Negative) / Total Mentions) x 100| Score | Interpretation |
|---|---|
| 70+ | Excellent - Highly positive perception |
| 40-70 | Good - Mostly positive |
| 0-40 | Mixed - Some concerns present |
| Below 0 | Negative - More negative than positive |
Sentiment Trends
Track how sentiment changes over time:- Is sentiment improving or declining?
- Do certain events affect sentiment?
- How does sentiment compare to visibility trends?
Sentiment by Context
By Platform
Different AI platforms may have different sentiment patterns:- ChatGPT: Based on OpenAI’s training data and guidelines
- Claude: Anthropic’s approach to recommendations
- Perplexity: Influenced by real-time web data
- Gemini: Google’s perspective and knowledge
By Prompt Type
Sentiment often varies by question type:- “Best” questions: Often more positive (top recommendations)
- Comparison questions: May highlight negatives for contrast
- Problem-solving questions: May mention limitations
By Topic
Analyze sentiment across different aspects:- Pricing sentiment
- Feature sentiment
- Support sentiment
- Ease of use sentiment
Common Sentiment Patterns
Conditional Recommendations
AI often provides conditional recommendations that affect sentiment:“Great for small teams, but may not scale well for enterprises”This is coded as mixed sentiment - positive for one use case, negative for another.
Comparative Sentiment
When your brand is compared to competitors:“More affordable than Competitor X, but with fewer features”Both positive and negative elements are captured.
Temporal Sentiment
AI may reference historical issues:“Previously had reliability issues, but has improved significantly”We capture the current implied sentiment while noting the context.
Improving Sentiment
Address Negative Mentions
If you see consistent negative patterns:- Identify the issues: What specific criticisms appear?
- Address root causes: Fix the underlying problems
- Update public information: Ensure accurate info is available
- Gather positive signals: Reviews, testimonials, case studies
Amplify Positives
Strengthen what AI already likes about you:- Identify positive patterns: What does AI praise?
- Create more content: Expand on these strengths
- Gather supporting evidence: Reviews, awards, data
- Make it discoverable: Ensure AI can find this content
Monitor Competitor Sentiment
Understanding competitor sentiment helps:- Identify their weaknesses you can highlight
- Learn from their strengths
- Find positioning opportunities