Identify assignment submission sentiment
using AI
Below is a free classifier to identify assignment submission sentiment. Just input your text, and our AI will predict the sentiment of assignment submissions - 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("assignment-submission-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/assignment-submission-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/assignment-submission-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 assignment submissions.
This pretrained text model uses a Nyckel-created dataset and has 15 labels, including Appreciative, Content, Disappointed, Dissatisfied, Enthusiastic, Frustrated, Hopeful, Indifferent, 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 assignment submissions).
Whether you're just curious or building assignment submission sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify assignment submission sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Student Feedback Analysis: This use case entails using the assignment submission sentiment identifier to analyze students' sentiments expressed in their written submissions. By categorizing submissions as positive, negative, or neutral, educational institutions can gain insights into students' engagement and satisfaction levels regarding course content and teaching methods.
- Early Warning System: Implement the sentiment identifier as an early warning system for identifying students who may be struggling. Negative sentiments in assignment submissions can signal a need for intervention, allowing educators to proactively reach out and provide support before academic performance declines significantly.
- Curriculum Improvement: Educational institutions can utilize sentiment analysis to assess the strengths and weaknesses of their curriculum. By evaluating the sentiments behind assignment submissions across various subjects, they can identify areas needing improvement and enhance the overall educational experience.
- Instructor Performance Evaluation: Leverage the sentiment identifier to assess instructor performance based on students’ written submissions. Positive or negative sentiments in assignments can provide qualitative data on teaching effectiveness, helping institutions make informed decisions regarding faculty evaluations and professional development.
- Peer Review Sentiment Tracking: Use the sentiment analysis tool in peer review processes to gauge students' attitudes towards their peers' work. Positive feedback will be identified and can be highlighted, while patterns of negative feedback can be monitored to inform students about their collaborative skills and attitudes.
- Sentiment-Driven Personalization: Incorporate the assignment submission sentiment identifier into a personalized learning platform. The analysis can help tailor content delivery and instructional approaches based on students' emotional feedback, helping educators address individual needs more effectively.
- Educational Trend Analysis: Utilize the sentiment identifier to analyze trends over time within assignment submissions across different cohorts. By aggregating sentiment data, educational institutions can identify emerging patterns in student attitudes toward course materials and adaptability to remote or hybrid learning environments.