Identify study material sentiment
using AI
Below is a free classifier to identify study material sentiment. Just input your text, and our AI will predict the sentiment of the study material. - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("study-material-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/study-material-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/study-material-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 study material..
This pretrained text model uses a Nyckel-created dataset and has 18 labels, including Disappointed, Discouraging, Encouraging, Enthusiastic, Favorable, Mixed, Negative, Neutral, Optimistic and Pessimistic.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of the study material.).
Whether you're just curious or building study material sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify study material sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Student Feedback Analysis: This function can analyze student feedback on study materials to gauge overall sentiment. By identifying positive and negative sentiments, educators can make informed adjustments to their course materials and improve the overall learning experience.
- Resource Improvement Insights: Educational institutions can use sentiment analysis to evaluate the effectiveness of textbooks and online resources. This helps in pinpointing which materials resonate well with students and which need enhancement or replacement.
- Course Material Development: Publishers can leverage sentiment classification to assess the potential reception of new study materials before release. By understanding how similar materials have been received in the past, they can tailor content to better align with student expectations.
- Curriculum Effectiveness Monitoring: Educators can implement this function to monitor sentiment across different subjects and topics within the curriculum. This analysis can highlight areas where students may struggle or excel, allowing for targeted support or curriculum adjustments.
- Early Warning System for Learner Engagement: By continuously analyzing sentiment in student discussions and interactions regarding study materials, institutions can establish an early warning system. Negative sentiment trends can signal declining engagement, prompting proactive measures to improve morale or content relevance.
- Custom Learning Pathways: Education technology platforms can utilize sentiment analysis to create personalized learning experiences. By identifying preferences and emotional responses to various study materials, the platform can recommend tailored resources that align with individual student needs.
- Marketing and Promotion Strategies: Publishers and educational services can analyze sentiment data to develop effective marketing strategies for their products. Understanding what aspects of study materials are most appreciated or criticized can help shape promotional messages and target audience outreach.