Identify academic paper sentiment
using AI
Below is a free classifier to identify academic paper sentiment. Just input your text, and our AI will predict the sentiment of academic papers across various categories. - 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("academic-paper-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/academic-paper-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/academic-paper-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 academic papers across various categories..
This pretrained text model uses a Nyckel-created dataset and has 10 labels, including Mixed, Negative, Neutral, Positive, Somewhat Negative, Somewhat Positive, Strongly Negative, Strongly Positive, Very Negative and Very Positive.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of academic papers across various categories.).
Whether you're just curious or building academic paper sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify academic paper sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Academic Research Evaluation: Researchers can use the academic paper sentiment identifier to evaluate the prevailing sentiment in their literature reviews. By analyzing the sentiment of previous studies, they can identify trends, biases, and gaps in the research landscape.
- Grant Proposal Assessment: Funding agencies can implement sentiment analysis on submitted grant proposals to assess the tone and optimism of the proposed research. This can help reviewers evaluate the likelihood of successful project execution and the researchers' confidence.
- Literature Review Automation: Academic institutions can develop automated systems that utilize sentiment analysis to generate summaries of scholarly articles. This can aid students and researchers in quickly identifying the emotional tone surrounding various studies, enhancing their understanding of the research context.
- Conference Paper Reviews: Conference organizers can utilize sentiment analysis on submitted papers to assist in the review process. By gauging sentiment, reviewers can prioritize papers that display innovative, optimistic, or constructive themes, fostering a positive conference atmosphere.
- Plagiarism and Misconduct Detection: Academic integrity offices can leverage sentiment analysis to identify potential cases of plagiarism or academic misconduct. Papers with inconsistencies in sentiment—such as an overly positive tone in the context of a critical review—may warrant further investigation.
- Curriculum Development: Educational developers can analyze the sentiment in academic publications related to new teaching methodologies or subjects. By identifying papers with a positive sentiment, they can curate content that aligns with progressive educational trends.
- Alumni and Donor Engagement: Universities can use sentiment analysis on publications to create engaging communications for alumni and donors. Highlighting positive sentiments related to research breakthroughs can foster stronger connections and enhance fundraising efforts.