Identify therapy session sentiment
using AI
Below is a free classifier to identify therapy session sentiment. Just input your text, and our AI will predict the overall sentiment of therapy session transcripts - 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("therapy-session-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/therapy-session-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/therapy-session-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the overall sentiment of therapy session transcripts.
This pretrained text model uses a Nyckel-created dataset and has 20 labels, including Angry, Anxious, Bored, Calm, Conflicted, Content, Disconnected, Dissatisfied, Empowered and Engaged.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the overall sentiment of therapy session transcripts).
Whether you're just curious or building therapy session sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify therapy session sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Client Session Review: The therapy session sentiment identifier can assist therapists in reviewing recorded sessions by analyzing the emotional tone of conversations. This helps therapists identify patterns in client emotions over time, enabling them to tailor future sessions more effectively.
- Post-Session Feedback: After a therapy session, clients can receive an automated summary of their emotional responses detected during the session. This summary can serve as an additional feedback mechanism, helping clients reflect on their feelings and share their perceptions of the session with their therapist.
- Progress Tracking: Therapists can use the sentiment identifier to track the emotional progress of clients over multiple sessions. By quantifying shifts in sentiment, therapists gain insights into whether clients are improving, facing challenges, or require adjustments in treatment approaches.
- Peer Supervision Support: In peer supervision or consultation groups, therapists can use the sentiment analysis to discuss cases with a focus on emotion rather than just content. This can lead to deeper insights and more collaborative approaches to client care, fostering an environment of shared learning.
- Research and Development: Mental health researchers can utilize the sentiment identifier to study the effects of different therapeutic approaches on client emotions. Data collected can inform the effectiveness of certain techniques, leading to improved therapy frameworks and training for practitioners.
- Crisis Detection: The sentiment identifier can help identify when a client is becoming increasingly distressed during sessions, allowing therapists to intervene proactively. This can be particularly valuable in high-risk situations, ensuring clients receive timely support when needed most.
- Marketing and Client Acquisition: Therapy practices can utilize insights from sentiment analysis in marketing strategies by showcasing their ability to address emotional needs effectively. By highlighting positive outcomes and emotional transformations in targeted marketing campaigns, practices can attract more clients seeking effective mental health support.