AI brand sentiment analysis tool for real-time customer insight
Brand sentiment analysis is the use of AI to read what people write about your brand and judge whether it is positive, negative or neutral, and why.
Brandnata scores every mention it collects, detects the emotions behind it, and tracks how sentiment shifts over time, so you can spot problems before they grow.
How does Brandnata analyze brand sentiment?
Keyword tools count words like “love” or “worst”. Brandnata reads the whole mention for meaning, emotion and intent, which is what lets it tell a genuine compliment from a sarcastic one.
“Ordered on Monday, arrived Wednesday, fits perfectly. Buying another color.”
“Does anyone know if the new collection ships internationally?”
“Great, another week and still no tracking update. Love that for me.”
The sarcasm test
The negative example above contains “great” and “love”. A keyword counter would score it positive. Reading it in context, it is a complaint about a late delivery. Each classification also carries a confidence score, so your team can review the uncertain ones.
A common way to score it: net sentiment
% positive mentions − % negative mentions
Brandnata shows a single sentiment score next to the full positive, negative and neutral split, so the headline number never hides what sits behind it.
Mention counts measure noise, not how people feel
A spike in mentions could be a viral compliment or the start of a backlash. Without sentiment, you can't tell which until someone reads them all.
Counting, not understanding
Mention counts show how loud a conversation is, not how people feel about you.
Late detection
Sentiment shifts show up in reports long after they start.
Unread feedback
Reviews and comments pile up faster than any team can read them.
Perception drives purchases
How people feel about a brand shapes whether they buy from it.
What can Brandnata detect in customer conversations?
Sentiment, emotion, trend and risk, scored on the mentions collected by the Brandnata brand monitoring tool.
AI sentiment classification
Machine learning models read each mention in context to classify brand sentiment.
- Positive, negative and neutral classification
- A confidence score on every prediction
- Context-aware reading of sarcasm, slang and emojis
- Sentiment detection in multiple languages
Emotion detection
Understand the emotions driving conversations about your brand.
- Emotions such as joy, anger, frustration, excitement and fear
- Emotional intensity scoring
- Emotion trend tracking
- Root cause identification
Sentiment trend tracking
Follow how brand and audience sentiment change over time.
- Daily, weekly and monthly sentiment trends
- Sentiment velocity, the rate of change
- Seasonal patterns
- Sentiment lined up against events and launches
Early warning for reputation risk
Catch the shifts that tend to come before a reputation problem.
- Abnormal sentiment spikes detected
- Rising risk of a reputation issue flagged early
- Escalation likelihood scoring
- Recommended response actions
Find the topic that is pulling opinion down
One review can praise the product and complain about the shipping. Averaged together, that looks neutral. Brandnata groups mentions into topics and scores each one, so a brand that is liked overall can still see the one area that needs fixing.
- Mentions grouped into themes such as product quality, pricing or delivery
- Sentiment tracked per topic, per source and over time
- Alerts when one topic starts trending differently from the rest
Why emotion detection matters
Two negative mentions can need very different responses. A frustrated customer wants a fix. An angry one may be about to post again, louder. Brandnata names the emotion behind each mention and scores its intensity, so support, PR and product teams know which conversations to pick up first.
Three steps from mentions to sentiment insight
Sentiment runs on the same mention feed as brand monitoring, so there is nothing extra to set up.
Collect mentions
Brandnata gathers mentions from social media, news, reviews and forums through its brand monitoring feed.
AI reads sentiment
Models classify each mention, detect the emotion behind it and group mentions into topics.
Act on what changed
Alerts and dashboards show where sentiment is moving and which topic is moving it.
The brand sentiment analysis dashboard
Six views that show how people feel, why, and how that compares with your competitors.
Sentiment score
One score for your brand, updated continuously and tracked over time.
Sentiment distribution
The positive, negative and neutral split at a glance.
Emotion wheel
An interactive view of the emotions behind audience sentiment.
Topic-sentiment matrix
Which topics lift brand perception and which pull it down.
Influencer sentiment impact
How influential voices affect your brand sentiment.
Competitive benchmarking
Your brand sentiment compared with competitors.
Who benefits from brand sentiment analysis
Teams whose decisions depend on how customers feel, not just how many of them are talking.
Brand managers
Track brand perception and sentiment trends.
Social media teams
Adjust strategy using up-to-date audience sentiment.
Product managers
Use customer feedback to decide what to fix or build next.
Customer experience teams
Find recurring pain points and improve retention.
Security and data controls
Read-only connections
Official platform APIs with OAuth or read-only API keys. No scraping and no password sharing.
Suggest mode first
Agents recommend and wait for approval. Every action is logged and reversible.
Your data stays yours
Never shared across brands and never used to train models for anyone else.
Brand sentiment analysis FAQs
What is brand sentiment analysis?
Brand sentiment analysis measures how people feel about your brand, not just how often they mention it. Brandnata uses AI and natural language processing to classify each mention as positive, negative or neutral and to detect the emotions behind it, such as frustration or excitement.
How is a brand sentiment score calculated?
A common method is net sentiment: the share of positive mentions minus the share of negative mentions over a period. Some teams also track the positive share on its own. Brandnata shows a single sentiment score next to the full positive, negative and neutral split, so you can see both the headline number and what sits behind it.
What is the difference between sentiment and emotion detection?
Sentiment tells you whether a mention is positive, negative or neutral. Emotion detection goes a step further and names the feeling, such as joy, anger, frustration or excitement. Two negative mentions can call for very different responses, one from a frustrated customer and one from an angry one, which is why Brandnata reports both.
How does Brandnata handle sarcasm, slang and context?
Brandnata's models read the whole mention rather than matching single keywords, which helps with sarcasm, slang and emojis. Each classification comes with a confidence score, so your team can review low-confidence mentions directly.
Can I see sentiment by topic, not just overall?
Yes. Brandnata groups mentions into topics and shows the sentiment for each one, so a brand that is liked overall can still see that one topic, such as delivery times or pricing, is pulling opinion down.
How does sentiment analysis help improve business decisions?
It shows what customers love or dislike and how that changes over time. Spotting a negative trend early gives product, support and marketing teams time to respond before it affects how people see your brand.
Where does the sentiment data come from?
Brandnata analyzes the mentions collected by its brand monitoring tool, covering social media, news, reviews and forums. Sentiment is tracked per source, so you can see when one channel is trending differently from the rest.
Understand how your audience really feels
Turn reviews, comments and coverage into a clear read on brand perception, and the topics behind it.
Prefer email? sales@perimattic.com