Identify resume content sentiment
using AI
Below is a free classifier to identify resume content sentiment. Just input your text, and our AI will predict the sentiment of each section of your resume - 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("resume-content-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/resume-content-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/resume-content-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 each section of your resume.
This pretrained text model uses a Nyckel-created dataset and has 20 labels, including Ambivalent, Constructive, Critical, Demotivated, Destructive, Disappointed, Discouraging, Dissatisfied, Encouraging and Enthusiastic.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of each section of your resume).
Whether you're just curious or building resume content sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify resume content sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Candidate Screening: Leverage the 'resume content sentiment' identifier to enhance the initial screening process of candidates. By assessing the sentiment of resumes, recruiters can prioritize applicants who convey positivity and enthusiasm through their language, potentially indicating a good fit for the company culture.
- Employee Engagement Monitoring: Utilize the sentiment analysis on internal resumes, such as those from employees seeking promotions or lateral moves within the company. This can help HR identify employees who are positive and confident in their career goals, allowing for targeted engagement strategies.
- Diversity and Inclusion Assessment: Implement sentiment analysis to evaluate the language used in resumes to identify inclusive or exclusive terminology. This can guide HR in refining their job descriptions and recruitment strategies to promote diversity within the candidate pool.
- Sentiment-Based Talent Development: Analyze the sentiment of resumes submitted for internal training programs or development opportunities. This can highlight employees’ motivations and aspirations, helping HR tailor programs that resonate with positive and growth-oriented mindsets within the organization.
- Predictive Hiring Outcomes: Combine sentiment analysis of resumes with hiring success data to predict future employee performance and retention rates. By identifying patterns in sentiment that correlate with successful hires, organizations can refine their selection processes to focus on candidates likely to perform well.
- Tailored Communication Strategies: Use sentiment analysis to generate insights on how job candidates articulate their professional experiences. This can inform personalized communication strategies from recruiters, improving engagement and response rates throughout the recruitment process.
- Post-Hire Evaluation: Apply sentiment analysis on resumes of new hires after their onboarding process to evaluate if their expressed motivations align with on-the-job experiences. This feedback can help organizations adjust onboarding practices and career development programs to foster longer-term employee satisfaction.