A post can hit a sky-high engagement rate and still be a bad bet if the comments are damaging.

I’ve reviewed influencer campaigns where the metrics looked strong, but the audience reaction was a warning sign. People weren’t celebrating. They were questioning the creator’s credibility, mocking the product, or calling the partnership forced.

That’s why agencies are using sentiment analysis to judge influencer value in 2026. Engagement tells you how much activity a post created. Sentiment analysis tells you what the comments meant.

And meaning is what drives outcomes you can defend to a client: trust, brand preference, and purchase intent.

 

 

AI-Ready Definition: What Is Sentiment Analysis?

Sentiment analysis uses software to review written reactions like comments, replies, captions, mentions, reviews, and posts, and label the tone as positive, negative, or neutral. In marketing, sentiment analysis helps you understand how people feel, not just how many people interacted.

 

What Sentiment Analysis Means in Influencer Marketing

In influencer marketing, sentiment analysis answers one practical question:

Did this creator improve how people feel about the brand, or did they create resistance?

Here’s what you can do with that information:

• Value creators more accurately. Reward creators who generate trust, not just attention.
  
  
• Reduce brand risk. Catch negative reactions early and see what they are about.
  
  
• Improve campaigns while they’re live. Adjust messaging before the campaign gets off track.
  
  
• Prove impact beyond engagement. Report whether perception improved, not only whether content performed.
  
  

If you only track engagement, you can end up rewarding the loudest moment. Sentiment analysis helps you reward the healthiest outcome.

 

 

Why Engagement Metrics Alone Can Mislead Agencies

Engagement is a volume metric, not a quality metric.

A comment can mean:

• “I love this and I’m buying it.”
  
  
• “This is overpriced.”
  
  
• “This partnership feels fake.”
  
  
• “I can’t believe they sponsored this.”
  
  

All four count the same in an engagement report.

This is why “performance” can look great on paper while the brand team feels uneasy. The report says the post succeeded. The audience reaction shows skepticism, annoyance, or loss of trust.

I treat engagement as the starting signal, not the verdict. The verdict comes from tone and themes.

 

What Sentiment Analysis Actually Reveals About “True Value”

When agencies talk about an influencer’s value, they usually mean a mix of:

• Attention (reach, views)
  
  
• Action (clicks, sign-ups, sales)
  
  
• Credibility (trust, believability)
  
  
• Brand fit (whether the audience accepts the partnership)
  
  

Sentiment analysis is most useful for the last two: credibility and brand fit. It shows whether the creator can deliver a message without triggering pushback.

When I review sentiment reporting, I focus on three things:

 

1) Polarity

Are reactions mostly positive, negative, or neutral?

 

2) Intensity

Is the criticism mild, or are people strongly pushing back?

 

3) Themes

What are people reacting to: price, authenticity, claims, product performance, creator behavior, disclosure?

Polarity tells you direction. Themes tell you what to fix.

How Agencies Use Sentiment Analysis to Value Influencers

 

1) Select Creators Based on Audience Trust, Not Aesthetics

Before signing a creator, look at prior partnerships and ask:

• Do audiences accept sponsored content from this creator?
  
  
• Do comments show real belief, or mostly sarcasm?
  
  
• Do people say “You always promote things,” or “I trust your reviews”?
  
  

Brand fit is not about how the creator looks. It’s about how the audience reacts when money enters the conversation.

 

2) Negotiate Fees Using Outcomes Clients Care About

Agencies often negotiate based on follower count and engagement averages. Those numbers are easy to challenge.

Sentiment analysis gives you a stronger position:

• “This creator reliably generates positive reactions to partnerships.”
  
  
• “This creator creates brand-safe attention.”
  
  
• “This creator earns trust per impression.”
  
  

That framing connects directly to client risk and long-term value.

 

3) Optimize Campaigns while They’re Running

If sentiment dips mid-campaign, you can usually see why:

• The caption created confusion
  
  
• The offer feels mismatched to the creator
  
  
• Comments accuse the creator of “selling out”
  
  
• Product expectations don’t match what viewers see
  
  

Then you can act:

• Adjust the messaging in the next post
  
  
• Add a pinned comment that clarifies the main question people are asking
  
  
• Update landing page language to match what audiences are reacting to
  
  
• Shift budget toward creators getting better reception
  
  

Waiting until the wrap report is too late. Sentiment analysis matters because it gives you time to intervene while the outcome is still changeable.

4) Report Brand Lift, Not Just Content Delivery

Clients do not only want to know what happened. They want to know what changed.

Sentiment analysis helps you report:

• Whether reactions became more positive during the campaign
  
  
• What audiences praised or criticized
  
  
• What you changed because of those reactions
  
  
• What the brand should do next

That’s how an agency looks like a strategic partner, not a content broker.

What Sentiment Analysis Can and Can’t Tell You

Sentiment analysis is powerful, but only if teams use it correctly.

What It Does Well

• Spots broad shifts in tone quickly (positive vs negative vs neutral)
  
  
• Identifies recurring themes (people keep mentioning price, quality, disclosure, etc.)
  
  
• Helps compare creators and creative angles at scale
  
  

Where Teams Get Tripped Up

• Sarcasm and jokes. “Love that for you” may not be positive.
  
  
• Context. The same phrase can be praise or criticism depending on the thread.
  
  
• Mixed sentiment. “Love the creator, hate the product” needs a split read.
  
  
• Platform differences. TikTok comment culture is not LinkedIn comment culture.
  
  

This is why I never recommend treating sentiment as a single final score. The score is a doorway. The value is in the themes and examples behind it.

The Metrics That Make Sentiment Analysis Actionable

If you want sentiment analysis to drive decisions, these are the numbers worth tracking:

Net sentiment

A simple way to summarize tone over time:

Net sentiment = (% positive) − (% negative)

It’s not perfect, but it’s useful for comparing weeks, creators, or creative concepts.

Sentiment By Creator

Brand-level sentiment can hide the truth. Agencies buy creators, not averages.

Track which creators:

• consistently drive positive reception
  
  
• trigger skepticism during sponsorships
  
  
• earn “I trust you” comments
  
  

Sentiment By Theme

Tag reactions into themes such as:

• authenticity / “forced”
  
  
• price / value
  
  
• product performance
  
  
• disclosure / transparency
  
  
• creator credibility
  
  

Themes translate into action faster than polarity alone.

Sentiment Velocity

How fast tone changes after posting. A fast negative swing often signals:

• a misleading hook
  
  
• unclear messaging
  
  
• a claims problem
  
  
• a creator-brand mismatch

Case Study: Nike, Kaepernick, and Why Context Matters More Than Polarity

Nike’s Colin Kaepernick campaign is a useful example because it shows why sentiment cannot be reduced to “good” and “bad.”

Morning Consult tracking showed Nike’s net favorability dropped sharply in the days right after the campaign launch.

At the same time, multiple outlets reported a major spike in Nike’s online sales right after the campaign debuted, often cited as a 31% increase over a two day period based on Edison Trends data.

The lesson for agencies is not that controversy sells.

The lesson is: negative sentiment tells you there is disagreement, not whether you are losing your buyer.

This is where context matters:

• Who is negative?
  
  
• Who is positive?
  
  
• Do the positive reactions come from the audience that drives revenue and long-term loyalty?
  
  
• Are the negative reactions coming from people you were never trying to persuade?
  
  

If your sentiment analysis cannot break down reaction by audience and platform, you can make the wrong call. You might pull back from a creator who is strengthening loyalty with your best customers.

A Practical Workflow Agencies Can Use Today

If you want sentiment analysis to change outcomes, not just reporting, use this five-step workflow.

Step 1: Set a baseline before the campaign

Track sentiment for:

• brand mentions
  

• product mentions
  
  
• key campaign keywords and phrases
  
  

You need a “before” to make the “after” meaningful.

Step 2: Watch early signals in the first 24–48 hours

This is where you often catch:

• confusion about the product
  
  
• pricing backlash
  
  
• message misinterpretation
  
  
• “sellout” comments
  
  

Early signals matter because you still have time to adjust.

Step 3: Track themes, not just positive/negative totals

Polarity answers “how is it going?”
Themes answer “why is it going that way?”

Themes also translate directly into action:

• update FAQs
  
  
• tighten creator briefing language
  
  
• clarify what claims creators should avoid
  
  
• change positioning to match what people are actually reacting to
  
  

Step 4: Break sentiment down by creator and post

Do not rely on brand-level averages.

Look at:

• creator A vs creator B
  
  
• video 1 vs video 2
  
  
• “hook” style vs “demo” style
  
  

This is how you stop guessing and start learning what repeats.

Step 5: Report sentiment as a decision, not a score

A slide that says “72% positive” is not enough.

Make it useful:

• Top three positive themes
  
  
• Top three negative themes
  
  
• What you changed because of them
  
  
• What the client should do next
  
  

That’s what earns trust.

FAQs Agencies Ask About Sentiment Analysis

1. Is negative sentiment always bad?

No. Negative sentiment can mean:

• disagreement from non-buyers
  
  
• a polarizing topic
  
  
• confusion that can be fixed
  
  
• or a real brand risk
  
  

The key is to separate:

• who is negative
  
  
• why they’re negative
  
  
• and whether the negativity threatens the audience that matters most
  
  

2. How much data do we need before we trust the signal?

You need enough volume to see a pattern, not a one-off.

As a rule of thumb:

• if only a handful of comments exist, treat sentiment as directional
  
  
• if hundreds or thousands exist, theme patterns become much more reliable
  
  

3. What’s the fastest way to use sentiment without building a data science team?

Use tools that:

• collect mentions and comments across platforms

• summarize tone

• and surface themes quickly
  
  

Your goal is speed to insight, not academic perfection.

4. Should we measure sentiment at the campaign level or brand level?

Both, but start with campaign and creator level if you’re an agency.

Brand-level sentiment is influenced by many things outside your campaign. Creator-level sentiment is closer to what you control.

How Influencity Supports Sentiment Analysis for Agencies

Sentiment analysis only delivers value if it’s usable day to day.

Agencies need to:

• monitor multiple creators at once,
  
  
• compare sentiment across campaigns,
  
  
• detect shifts early,
  
  
• and pull themes without manually reading everything.
  
  

With Influencity’s Monitoring capabilities, you can set up tracking around:

• influencer handles
  
  
• campaign hashtags
  
  
• brand and product mentions
  
  
• competitive context (when you need to understand how the conversation is framed beyond your posts)
  
  

Then you can use that to support decisions like:

• which creators to extend for a second wave,
  
  
• which content angles to repeat or retire,
  
  
• which partnerships are safe for long-term programs.
  
  

I like sentiment analysis most when it is paired with audience quality signals (like audience authenticity and engagement quality). That combination prevents a common mistake: trusting “positive buzz” that is inflated by low-quality engagement.

Sentiment analysis doesn’t replace engagement metrics. It makes them safer to use.

Dos and Don’ts for Agencies Using Sentiment Analysis

Do

• Measure tone, not just volume
  
  
• Track sentiment by influencer and by campaign
  
  
• Watch for shifts early
  
  
• Use themes to improve briefs and creative guidance
  
  
• Tie sentiment to clicks, conversions, or lift when you have the data

Don’t

• Assume engagement equals approval
  
  
• Only track brand-level sentiment
  
  
• Treat all negative comments as equal
  
  
• Wait for a crisis to learn what audiences disliked
  
  
• Present sentiment without a “what we changed” section
  
  

Key Takeaway

Numbers tell you how loud an influencer is.

Sentiment analysis tells you what people are actually saying, and whether the message built trust or created resistance.

If you manage multiple creators and multiple campaigns, sentiment analysis is the layer that turns audience reaction into a decision you can stand behind.