Identify voicemail transcript sentiment
using AI
Below is a free classifier to identify voicemail transcript sentiment. Just input your text, and our AI will predict the sentiment expressed in a voicemail transcript - in just seconds.
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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("voicemail-transcript-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/voicemail-transcript-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/voicemail-transcript-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the sentiment expressed in a voicemail transcript.
This pretrained text model uses a Nyckel-created dataset and has 20 labels, including Calm, Confident, Critical, Disappointed, Doubtful, Encouraging, Frustrated, Happy, Mixed and Negative.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment expressed in a voicemail transcript).
Whether you're just curious or building voicemail transcript sentiment detection into your application, we hope our classifier proves helpful.
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Need to identify voicemail transcript sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Service Enhancement: This function can be utilized in customer service departments to analyze voicemail transcripts from customers. By identifying sentiment, companies can prioritize callbacks based on urgency and satisfaction levels, ensuring timely and appropriate responses to customer needs.
- Marketing Campaign Optimization: Marketers can use sentiment analysis on voicemail transcripts to gauge customer reactions to specific campaigns or offers. By understanding whether customers express positive or negative sentiments, businesses can tailor their marketing strategies for improved outreach and engagement.
- Employee Feedback Analysis: Organizations can employ this technology to analyze voicemails left by employees regarding workplace conditions. By understanding employee sentiment, HR teams can identify potential issues and implement solutions to enhance job satisfaction and retention.
- Risk Management: Financial institutions can use this feature to classify the sentiment of voicemails from clients discussing their accounts or financial portfolio. Detecting negative sentiments early can help in mitigating risks associated with client dissatisfaction or potential account closures.
- Sales Follow-Up Strategy: Sales teams can analyze voicemails left by prospects to determine their interest level based on emotional tone. This information can help reps prioritize leads and customize their follow-up approach for higher conversion rates.
- Crisis Management: In situations where customers leave voicemails during a crisis, sentiment analysis can provide crucial insights into public perception. Organizations can then respond swiftly to negative sentiments, demonstrating empathy and commitment to resolving issues.
- Voice of the Customer Programs: Companies can incorporate sentiment analysis from voicemail transcripts as part of their Voice of the Customer initiatives. By aggregating sentiment data, businesses can identify trends and patterns in customer opinion, driving product improvements and enhancing customer satisfaction.