Identify peer review sentiment
using AI
Below is a free classifier to identify peer review sentiment. Just input your text, and our AI will predict the sentiment of peer reviews it analyzes - 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("peer-review-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/peer-review-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/peer-review-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 peer reviews it analyzes.
This pretrained text model uses a Nyckel-created dataset and has 20 labels, including Approving, Critical, Criticism, Disapproving, Discouraging, Encouraging, Favorable, Mixed, Negative and Neutral.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of peer reviews it analyzes).
Whether you're just curious or building peer review sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify peer review sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Academic Paper Review Analysis: This function can assess the sentiment of peer reviews for academic papers to determine the overall tone and reception of submissions. By analyzing the sentiment, authors and editorial teams can gain insights into recurring feedback themes and improve the quality of future submissions.
- Journal Selection Optimization: Publishers can use sentiment analysis to evaluate peer reviews and choose journals that align with the tone of their submissions. This increases the chances of acceptance and helps authors target the most suitable publications for their work.
- Reviewer Feedback Enhancement: Academic institutions can utilize this sentiment identifier to analyze feedback from peer reviewers received by faculty members. This can guide training sessions to enhance the ability of reviewers to provide constructive criticism and maintain a positive tone in their assessments.
- Grant Proposal Evaluation: Research organizations can apply this function to gauge the sentiment of peer reviews for grant proposals. By understanding the sentiment, organizations can refine the application process and provide targeted feedback to applicants on areas needing improvement.
- Conference Submission Insights: Conference organizers can leverage sentiment analysis to evaluate feedback from peer reviews on submitted presentations and posters. This allows them to identify trends in reviewer sentiments to enhance the selection process and improve overall conference quality.
- Identify Trends in Research Disciplines: Analyzing the sentiment of peer reviews across different disciplines can help organizations spot trends and shifts in academic opinions. This information can inform researchers and institutions about evolving areas of interest and potential funding opportunities.
- Monitor Reviewer Behavior: By assessing reviewer sentiments over time, academic institutions can monitor the behavior and biases of peer reviewers. This can help ensure fair and balanced reviews, addressing any potential issues in the review process to uphold the integrity of academic publishing.