Identify medical review sentiment
using AI
Below is a free classifier to identify medical review sentiment. Just input your text, and our AI will predict the sentiment of medical reviews - 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("medical-review-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/medical-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/medical-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 medical reviews.
This pretrained text model uses a Nyckel-created dataset and has 8 labels, including Mixed, Negative, Neutral, Positive, Somewhat Negative, Somewhat 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 medical reviews).
Whether you're just curious or building medical review sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify medical review sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Patient Feedback Analysis: This function can be utilized to analyze sentiments in patient feedback collected through surveys or online reviews. By identifying the sentiment, healthcare providers can gain insights into patient satisfaction and areas needing improvement.
- Drug Efficacy Reviews: Pharmaceutical companies can use the sentiment identifier to analyze reviews from patients regarding specific medications. This analysis can help in assessing perceived efficacy and side effects, contributing to better safety profiles and marketing strategies.
- Social Media Monitoring: Healthcare organizations can monitor social media platforms for patient discussions about treatment experiences. The sentiment classification can help gauge public opinion and identify potential issues or trends related to public health campaigns.
- Clinical Trial Feedback: Researchers can employ the sentiment classification to evaluate comments and opinions from participants in clinical trials. Understanding participant sentiment can provide valuable feedback about their experiences, promoting better trial management and potential improvements in patient engagement.
- Telehealth Session Reviews: After telehealth appointments, patients often leave reviews about their experiences. The sentiment identifier can help healthcare providers assess how well they are meeting patient needs during virtual visits, enabling them to refine telehealth services.
- Medical Equipment Evaluations: Manufacturers of medical devices can analyze sentiment from users regarding their products. Understanding user perceptions can directly influence product design, support services, and customer satisfaction initiatives.
- Public Health Campaign Effectiveness: Public health officials can track sentiments towards health campaigns by analyzing comments in media or community forums. This data can inform campaign adjustments, making initiatives more responsive to public opinion and enhancing community engagement.