Words have power. They can make people laugh, rage-tweet, or leave a 1-star review that ruins your brand’s whole week.
That’s why companies today use sentiment scoring — a way for AI to scan text and figure out if people sound happy, angry, or just plain bored with regards to your brand or even local business.
AI can’t feel emotions. But it’s pretty good at spotting when you do.
What is an LLM?
An LLM (Large Language Model) is a big, word-hungry computer system trained to read and write like a person. You know it as your AI chat (ChatGPT, Gemini, Perplexity, etc).
It has seen just about everything online — from Wikipedia articles to Reddit arguments about pineapple on pizza. (It doesn’t belong on it, I will die on this hill.)
When it reads a sentence like:
“This phone app is the best thing since sliced bread,” it thinks, “Positive emotion detected.”
When it reads:
“This app crashed faster than my New Year’s goals,” it sighs (digitally) and marks it as “Negative.”
So no, it’s not psychic — just very observant.
What Does “Sentiment Scoring” Mean?
Sentiment scoring is how computers turn feelings into math.
- Positive (+1): Happy humans.
- Neutral (0): No one cares that much.
- Negative (-1): Sad or angry humans.
If someone writes,
“The product works, but it smells like old socks,” the score might land near -0.3 — slightly unhappy, mildly traumatized.
These numbers help brands see the emotional trends behind their mentions, reviews, and comments.
This kind of insight has never been available until now, and certainly not evident in conventional SEO tracking.
Where Does the Data Come From?
AI learns from millions of human posts scattered across the internet — basically everywhere people love to share opinions.
Common sources include:
- Social Media: Twitter, Reddit, Facebook – a constant stream of joy, sarcasm, and chaos.
- Product Reviews: Amazon, Yelp, Google, and app stores – where the most honest (and dramatic) emotions live.
- Forums & Community Boards: Niche discussions about every topic under the sun.
- Customer Feedback Forms: Direct responses from users, often written in all caps.
- Surveys and Support Chats: Polite messages, or not-so-polite ones, telling companies how they did.
In short, anywhere humans post their unfiltered thoughts is fuel for training sentiment models.
How AI Figures It Out
Here’s a simple version of how sentiment scoring works:
- You post or write something.
- The AI model breaks it into pieces (words, phrases).
- Each piece gets analyzed for tone — happy, sad, angry, or neutral.
- The AI model calculates an overall score.
For example:
“The delivery was late, but the driver was friendly.”
Might land around +0.1 — slightly positive, because nice humans cancel out late boxes.
It’s all pattern recognition. AI isn’t “understanding” feelings — it’s just very good at guessing based on history.
AI’s Biggest Weakness: Sarcasm
If robots had an arch-nemesis, it would be sarcasm.
Take this line:
“Oh, great, another feature that totally doesn’t work.”
Humans read it and hear frustration. The model might score it as cheerful enthusiasm. Oops.
Sarcasm, mixed emotions, and cultural slang are where sentiment models still stumble. But as they learn from more real-world data (like Reddit threads full of snark), they’re improving.
Why Your Brand/Business Should Monitor Your Sentiment Score
Now for the part that actually matters — why you should care.
Your customers are already talking. Everywhere. Whether it’s praise, complaints, or memes about your new logo, those conversations shape your reputation.
Sentiment scoring helps you listen at scale. Here’s why monitoring it matters:
You Catch Problems Before They Become Problems
If hundreds of posts start turning negative, something’s wrong — maybe an update failed, a shipment got delayed, or a customer support agent had a bad day.
By watching sentiment trends, brands can spot those dips and fix them before they become headline news.
You See What People Actually Like
Positive trends show what’s working. Maybe customers love your packaging or your new feature. Knowing that helps you do more of what people enjoy — without guessing.
You Measure Campaign Impact
After a product launch or ad campaign, you can track how people react in real time.
Do people sound excited or irritated? Sentiment scores give you that answer without waiting for sales numbers to roll in.
You Protect Your Brand Image
A single viral complaint can do serious damage. Monitoring public mood helps you jump in quickly, respond with care, and show customers you’re paying attention.
You Keep Leadership Informed
Instead of saying “people seem unhappy,” you can bring numbers — actual sentiment data that shows how customers feel, and how that changes over time.
Even executives like charts that talk about emotions.
Where to Track Sentiment
To make this useful, brands often track sentiment in specific places, such as:
- Social Listening Tools: To monitor mentions on platforms like Twitter, TikTok, and Reddit.
- Customer Review Dashboards: To watch new product feedback roll in.
- Helpdesk Platforms: To measure tone in support emails and chat logs.
- Survey Analysis: To summarize customer satisfaction at scale.
Think of it as emotional weather tracking — you can’t control the forecast, but you can dress for it.
The Takeaway
AI sentiment scoring isn’t magic. It’s pattern-spotting wrapped in math, built to help humans understand each other better (or at least try to).
It won’t always know when you’re being sarcastic, and it might confuse frustration with joy, but it’s still one of the fastest ways to read a crowd.
And for brands, that means no more guessing what customers think — the data tells you when they’re cheering, sighing, or sharpening their complaint tweets.
Just remember: the robot doesn’t have feelings… but your customers definitely do.