Having a conversation with a chatbot is one thing, but what happens when you're frustrated, trying to get an answer, and it doesn’t seem to get your urgency or annoyance? You’d probably close the chat thinking, “Well, that was useless. I wish it could understand my emotions and not just say the same thing over and over again, without a resolution.”
Here’s where sentiment analysis can change the game and make your agent not just smart, but emotionally intelligent. And as a company owner or brand dealing with people and their emotions on the daily, you know how crucial that is to get right. People only return to brands they’ve had good experiences with, and avoid the ones that leave a bad taste.
We’ve just introduced this feature to Copilot.live, and as I was writing our latest product update emailer, I thought: I have to talk about this more.
What is sentiment analysis?
Sentiment analysis lets your chatbot understand not just what someone said, but how they’re feeling when they say it.
In contrast to just processing text, this allows an agent to detect tone, whether someone sounds happy, angry, confused, or calm, and change its reply based on how the person feels.
For example, after not getting the resolution they wanted, a frustrated user might type, “So there’s NO RESOLUTION? Wow, thanks for nothing!”
Without sentiment analysis, the bot might reply, “You’re welcome! Let me know if you need anything else.” This will probably aggravate the user more and make them angrily shut the chat, rating the conversation poorly.
But with sentiment analysis, the bot picks up the sarcasm and frustration, changes tone, or escalates and hands over the conversation to a human.
That’s the power of good sentiment analysis. As chatbots and conversational agents become more popular, optimizing for human emotions is crucial.
About Sentiment Analysis in Copilot.live
You can find sentiment analysis live in the “Inbox” section of your account under the “Conversation Info” section. Each chat has its sentiment analysis window, and you can even check out sentiments from voice calls!
Our sentiment analysis is powered by machine learning and LLMs trained on large datasets that reflect how emotions appear in real conversations. Each chat is analyzed every 10 user messages or once an hour, so the insights stay fresh.
Types of emotions Copilot.live can detect
Most sentiment analysis models, including Copilot.live, break emotions into three categories. These give your bot just enough emotional intelligence to avoid saying the wrong thing at the worst time.
1. Positive
The user feels happy, calm, or satisfied.
They’ve likely gotten what they needed and are wrapping up.
Example:
“Awesome, thanks a lot!”
“Great, that solves it.”
“Appreciate the quick reply.”
2. Negative
The user is frustrated, annoyed, angry, or sarcastic.
Things didn’t go as expected, or they’re tired of repeating themselves.
Example:
“This is ridiculous, I’ve had enough.”
“So there’s NO resolution? Wow, thanks for nothing.”
“Your support is useless.”
3. Neutral
The user is asking a question or sharing information.
No emotion, just getting something done.
Example:
“Can you tell me when my order ships?”
“I need to update my address.”
“What’s your return policy?”
Even this simple emotional layer gives your chatbot a huge advantage in handling conversations.
Smart scoring across every conversation
We also track six key markers that show how well a conversation went.
1. Agent empathy
Did the agent acknowledge the user’s emotion and change its approach accordingly?
2. Bot persona adherence
Did the bot stay in character, or start sounding different halfway through?
3. Conversation engagement
Was the user actively involved in the conversation?
4. Emotional shift
Did the sentiment change? Did an angry user calm down? Did a calm user get annoyed?
5. Resolution effectiveness
Did the issue get resolved successfully?
6. Response relevance
Did the replies make sense given the question? Or was the agent off-topic?
Auto topic classification
We also automatically classify conversations by topic. Things like General assistance, Tech support, order issues, etc. That means you’re not just seeing sentiment, but also what users were emotional about. Super useful for spotting recurring problem areas.
Built-in tips for better conversations
Copilot.live now shows smart tips after every conversation, highlighting what could have gone better. These are based on sentiment, tone shifts, and how the agent handled the exchange.
What can you do with all of this data?
Sentiment scores, tips, and topic tags give you more than just a history of chats. They help you take action, improve performance, and spot issues faster.
- Train your agents to handle things better
When a response doesn’t land well, you can update your agent’s instructions or tweak the logic. It helps shape better conversations moving forward.
- Find what’s broken and where users struggle
When negative sentiment keeps showing up around the same topics, it’s a clear signal. Maybe a feature’s confusing, a flow is broken, or expectations aren’t being met. Topic and tone data help you catch it early.
- Make handovers easier
Tips and sentiment insights give the next agent a quick snapshot of the situation. They pick up right where the last one left off, without confusion.
- Coach your team with real conversations
Use flagged chats to highlight positive examples or show where things fell short. This makes coaching clear, practical, and grounded in real scenarios.
All that’s left is to try it out
That wraps up everything you need to know about sentiment analysis in Copilot.live.
I hope this explains not just what sentiment analysis is, but also how you can use it to improve the answers, resolutions, and ultimately the customer experience your agents deliver.
Happy building!
