Identify support ticket sentiment
using AI
Below is a free classifier to identify support ticket sentiment. Just input your text, and our AI will predict the sentiment of support tickets. - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("support-ticket-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/support-ticket-sentiment/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-ticket-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the sentiment of support tickets..
This pretrained text model uses a Nyckel-created dataset and has 6 labels, including Mixed, Negative, Neutral, Positive, Very Negative and Very Positive.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of support tickets.).
Whether you're just curious or building support ticket sentiment detection into your application, we hope our classifier proves helpful.
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Need to identify support ticket sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Support Optimization: By identifying the sentiment of support ticket sentiments, businesses can prioritize tickets based on urgency and emotional tone. This allows customer support teams to address negative feedback quickly, improving customer satisfaction and retention.
- Automated Response Routing: The sentiment identification function can route tickets to specialized teams based on the emotional tone of the message. For instance, tickets flagged with negative sentiment can be automatically assigned to senior-level support agents trained in conflict resolution.
- Trend Analysis for Product Improvement: Analyzing the sentiment of support tickets over time helps businesses identify trends related to specific products or services. This insight can guide product development teams in addressing recurring issues and enhancing overall customer experience.
- Real-Time Customer Insights: The function can provide real-time insights into customer sentiments, allowing businesses to adjust their strategies dynamically. If a sudden spike in negative sentiment is detected, immediate actions can be implemented to mitigate potential fallout.
- Performance Assessment of Support Agents: By correlating ticket sentiment with resolution outcomes, businesses can evaluate the performance of individual support agents. This data can inform training and development programs, rewarding agents who handle negative situations effectively.
- Proactive Customer Engagement: Identifying negative sentiments proactively can lead to preemptive engagement with customers. Businesses can reach out to dissatisfied customers to apologize, offer solutions, or gather more feedback before their concerns escalate.
- Sentiment-Driven Marketing Strategies: Understanding the sentiment of customer tickets can inform marketing departments about customers' feelings toward certain products or campaigns. This information can refine marketing messaging and identify opportunities for campaigns aimed at improving overall perception.