Identify interview transcript sentiment
using AI
Below is a free classifier to identify interview transcript sentiment. Just input your text, and our AI will predict the sentiment of the interview responses - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("interview-transcript-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/interview-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/interview-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 of the interview responses.
This pretrained text model uses a Nyckel-created dataset and has 21 labels, including Anxious, Confident, Content, Critical, Disappointed, Disheartened, Dissatisfied, Doubtful, Enthusiastic and Excited.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of the interview responses).
Whether you're just curious or building interview transcript sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify interview transcript sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Recruitment Analysis: The "interview transcript sentiment" identifier can be utilized by HR departments to assess candidate attitudes and emotional engagement during interviews. By analyzing transcripts, hiring managers can identify candidates who exhibit positive sentiments, helping to streamline the selection process.
- Interview Training Programs: Training organizations can leverage sentiment analysis to improve their interview coaching programs. By evaluating interview transcripts, trainers can pinpoint common emotional pitfalls and coach candidates on how to express themselves more positively and confidently.
- Continuous Improvement Feedback: Companies can implement sentiment analysis on interview transcripts to gather insights about the interview process itself. This analysis can highlight areas where interviewer techniques might be perceived negatively, guiding improvements to enhance candidate experience.
- Team Dynamics Assessment: Businesses can analyze interviews of potential team members to gauge how well their sentiment aligns with existing team culture. This aids in making informed hiring decisions that promote cohesion and positive team dynamics.
- Content Generation for Reports: The sentiment analysis can generate actionable insights for internal reports on recruitment processes. By providing metrics on candidate sentiment trends over time, organizations can better understand hiring challenges and adapt their strategies accordingly.
- Diversity and Inclusion Initiatives: Organizations focused on improving diversity can use sentiment analysis to evaluate how different groups feel during interviews. This can help identify biases in the interview process and inform strategies to create more inclusive hiring practices.
- Candidate Experience Enhancement: Companies can track sentiment over various interviews to measure candidate experience. By analyzing sentiments, businesses can develop tailored follow-up strategies for engagement and retention, which can ultimately enhance overall candidate satisfaction.