Sentiment Analysis: Quantifying Your Brand's Premium Perception by AI

When ChatGPT talks about your brand, does it use words like "premium," "excellence," "reference"?

Or rather "basic," "alternative," "affordable"?

This difference is called sentiment. And it's measurable.

What is AI Sentiment?

Definition

AI sentiment is the overall perception that language models have of your brand. It's the synthesized opinion from all sources they've ingested.

How It Forms

AI doesn't really "think." It reflects source consensus:

AI Sentiment = f(Sources ingested × Authority × Consistency)

If your sources are:

• Consistent: All say "premium" → Premium sentiment
• Inconsistent: Some say "premium," others "accessible" → Fuzzy sentiment
• Negative: Dominant criticism → Negative sentiment

Why It's Crucial

Sentiment determines mention tone. A brand with good sentiment is:

• Cited with valorizing terms
• Recommended rather than just mentioned
• Positioned favorably vs competitors

Premium Sentiment Dimensions

1. Quality Perception

Positive indicators:

• "high quality," "excellence," "craftsmanship"
• "market reference," "high standards"
• "premium materials," "impeccable finishes"

Negative indicators:

• "variable quality," "questionable value"
• "good but not exceptional"
• "better quality alternatives exist"

2. Legitimacy Perception

Positive indicators:

• "since [year]," "heritage," "history"
• "recognized by experts," "award-winning"
• "market leader," "pioneer"

Negative indicators:

• "newcomer," "still proving itself"
• "less established than," "challenger"
• "recent reputation"

3. Value Perception

Positive indicators:

• "investment," "heritage value"
• "justifies its price," "excellent value"
• "quality justifying positioning"

Negative indicators:

• "expensive for what it is," "high prices"
• "cheaper alternatives available"
• "questionable premium"

4. Innovation Perception

Positive indicators:

• "innovative," "pioneer," "avant-garde"
• "cutting-edge technology," "R&D"
• "defines trends"

Negative indicators:

• "traditional," "conservative"
• "behind on," "caught up by"
• "limited innovation"

How to Measure Sentiment

Method 1: Manual Analysis

Ask AIs questions and analyze responses:

Test questions:

• "What do you think of [brand]?"
• "How would you position [brand] in its market?"
• "Is [brand] considered premium?"
• "What are [brand]'s strengths and weaknesses?"

Analysis grid:

Method 2: Systematic Analysis

Test on a large query sample and quantify:

Sentiment Score = (Positive mentions × 2 + Neutral × 1 - Negative × 2) / Total × 100

Example:

• 50 queries tested
• 30 positive mentions, 15 neutral, 5 negative
• Score = (30×2 + 15×1 - 5×2) / 50 = 65/100

Method 3: Automated Tool

Reference Levels

Optimizing Sentiment

Step 1: Diagnose Sources

Identify what's "pulling down":

Sources to audit:

• Wikipedia (if existing)
• Major press articles
• Google/Trustpilot reviews
• Social profiles (LinkedIn, Twitter)
• Comparison sites and buying guides
• Your own site

Step 2: Correct Inconsistencies

If your site says "premium" but comparators say "mid-range," AI will be confused.

Actions:

• Align messaging across all controllable sources
• Contact third-party sources for factual corrections
• Enrich positive sources (more content = more weight)

Step 3: Amplify Positives

Mentions on authoritative sources carry more weight:

High-authority sources:

• Recognized sector media
• Professional publications
• Official awards and recognition
• Identified expert testimonials

Step 4: Track Evolution

Sentiment isn't static. Measure monthly:

Month 1: 55/100
Month 2: 58/100 (↑3 after site optimization)
Month 3: 62/100 (↑4 after press article)
Month 4: 68/100 (↑6 after Wikipedia update)

Concrete Case: Watch Brand

Initial Situation

Brand: Swiss watch manufacture, 150 years of history Problem: Mixed sentiment despite premium positioning

AI verbatim audit:

"Historic brand offering quality watches, although prices are high compared to some competitors offering similar specifications."

Diagnosis:

• Quality: recognized (positive)
• Price: perceived as high (negative)
• Differentiation: unclear (problematic)

Actions Taken

1. Wikipedia enrichment: Added exclusive innovations and patents
2. Press relations: Master watchmaker interviews on craftsmanship
3. Website: Highlighted technical differentiators
4. LinkedIn: Regular posts on heritage and unique pieces

Results at 4 Months

New AI verbatim:

"Exceptional watch manufacture, recognized for its centuries-old craftsmanship and patented innovations. An investment for discerning collectors."

Score evolution:

• Quality: 4 → 5
• Legitimacy: 3 → 5
• Value: 2 → 4 (from "expensive" to "investment")
• Innovation: 4 → 5

Sentiment and Competition

Competitive Benchmark

Your sentiment alone means nothing. Compare:

Gap Analysis

Identify what top scorers do differently:

• More authoritative sources?
• More consistent messaging?
• Clearer differentiators?

Mistakes to Avoid

Confusing Volume and Sentiment

Being heavily cited with poor sentiment is worse than rarely cited positively.

Ignoring "Secondary" Sources

AI doesn't always distinguish primary from secondary sources. A negative article on a small site can impact.

Neglecting Internal Consistency

If your site says "exclusive" but LinkedIn says "accessible to all," AI perceives the inconsistency.

Expecting Immediate Results

Sentiment evolves slowly. Models don't update instantly.

Conclusion

AI sentiment isn't a mystery. It's the measurable reflection of what your sources communicate.

For premium brands, good sentiment means:

• Being described with the right words
• Being recommended to the right prospects
• Being positioned correctly vs competitors

Optimizing it requires work on sources, consistency, and patience.