Identify feedback sentiment
using AI
Below is a free classifier to identify feedback sentiment. Just input your text, and our AI will predict the sentiment of the feedback provided. - 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("feedback-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/feedback-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/feedback-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 the feedback provided..
This pretrained text model uses a Nyckel-created dataset and has 14 labels, including Disappointed, Dissatisfied, Enthusiastic, Frustrated, Hopeful, Mixed, Negative, Neutral, Positive and Satisfied.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of the feedback provided.).
Whether you're just curious or building feedback sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify feedback sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Support Optimization: This use case focuses on analyzing customer feedback received through support channels to identify sentiment. By classifying feedback as positive, negative, or neutral, businesses can prioritize responses and improve customer satisfaction, ultimately reducing churn rates.
- Product Development Insights: The sentiment analysis can be applied to customer reviews and feedback on products. This information helps product teams to understand market reception, identify pain points, and guide future product enhancements based on negative sentiment trends.
- Marketing Campaign Assessment: Marketers can utilize sentiment classification to evaluate the effectiveness of their campaigns. By analyzing feedback and social media responses, they can measure public sentiment towards specific ads and make data-driven adjustments to optimize engagement.
- Brand Reputation Management: Companies can monitor sentiment across various platforms to gauge public perception of their brand. Identifying negative trends early allows for proactive reputation management and targeted communications to rectify potential issues before they escalate.
- Employee Engagement Analysis: Analyzing employee feedback collected through surveys or internal communication can help HR departments assess overall employee sentiment. Understanding workforce morale enables organizations to implement necessary changes and foster a more positive work environment.
- Service Quality Evaluation: Businesses can classify sentiment in service-related feedback to evaluate service quality performance. This insight allows organizations to identify areas for improvement in service delivery and drive initiatives that enhance customer experiences.
- Event Feedback Analysis: After events, organizers can analyze attendee feedback to gauge sentiment regarding the event’s success. This classification can provide valuable insights for future event planning and highlight specific areas that resonated with participants or require changes for better reception.