Identify support chat emotion
using AI
Below is a free classifier to identify support chat emotion. Just input your text, and our AI will predict customer emotions in support chats - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("support-chat-emotion", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/support-chat-emotion/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/support-chat-emotion/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict customer emotions in support chats.
This pretrained text model uses a Nyckel-created dataset and has 16 labels, including Angry, Anxious, Bored, Confused, Content, Curious, Disappointed, Excited, Frustrated and Happy.
We'll also show a confidence score (the higher the number, the more confident the AI model is around customer emotions in support chats).
Whether you're just curious or building support chat emotion detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify support chat emotion at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Support Sentiment Analysis: This use case involves analyzing customer support chat interactions to identify emotions such as frustration, satisfaction, or confusion. By categorizing these emotions, businesses can better understand customer sentiment and improve response strategies accordingly.
- Agent Performance Evaluation: The emotion identifier can be used to assess the performance of support agents by evaluating how effectively they handle customer emotions. By correlating agent responses with identified customer emotions, companies can develop training programs to enhance agent empathy and efficiency.
- Proactive Customer Engagement: By identifying negative emotions in chats, businesses can proactively reach out to customers to resolve issues before they escalate. This use case helps improve customer satisfaction and retention by addressing problems quickly and effectively.
- Personalized Customer Experience: The emotion detection functionality can enhance personalization in customer interactions by tailoring responses based on the detected emotions. For instance, a customer expressing frustration could receive a more empathetic and supportive message, improving overall interaction quality.
- Market Research and Insights: Businesses can leverage emotion analysis from support chats to gather insights into customer feelings about their products or services. This data can inform marketing strategies and product development, helping companies adjust offerings to better align with customer expectations.
- Identifying Bottlenecks in Support Processes: By analyzing chat emotions, organizations can identify common pain points or bottlenecks in their support processes. This insight allows for targeted improvements in both systems and training, ultimately leading to more efficient and effective customer support.
- Feedback Loop for Product Development: Emotions expressed in support chats can be categorized and aggregated to provide feedback on product features or services. This aggregated data can inform product teams about customer satisfaction or dissatisfaction, guiding enhancements and future developments based on real user experiences.