Identify meeting transcript sentiment
using AI
Below is a free classifier to identify meeting transcript sentiment. Just input your text, and our AI will predict the sentiment conveyed in meeting 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("meeting-transcript-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/meeting-transcript-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/meeting-transcript-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the sentiment conveyed in meeting transcripts.
This pretrained text model uses a Nyckel-created dataset and has 12 labels, including Critical, Cynical, Disapproving, Enthusiastic, Hopeful, Indifferent, 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 conveyed in meeting transcripts).
Whether you're just curious or building meeting transcript sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify meeting transcript sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Feedback Analysis: By analyzing meeting transcripts of customer feedback sessions, businesses can gauge the sentiment of their clients. This can help organizations identify areas of improvement and develop strategies to enhance customer satisfaction.
- Employee Engagement Monitoring: Organizations can use sentiment analysis on meeting transcripts to assess employee morale and engagement levels. Understanding employees' sentiments can inform leadership strategies to foster a more positive workplace environment.
- Conflict Resolution: Meeting transcripts can be analyzed to detect negative sentiments that indicate potential conflicts. By identifying these sentiments early, teams can intervene and address issues before they escalate.
- Sales Presentation Evaluation: Sales teams can leverage sentiment analysis to evaluate the effectiveness of their presentations by interpreting client reactions in meetings. Positive or negative sentiments expressed during discussions can guide sales strategies and training.
- Product Development Feedback: R&D teams can analyze sentiments expressed during brainstorming meetings to gain insights into team member enthusiasm or concerns regarding new products. This feedback loop can drive better product design and feature development.
- Stakeholder Communication Analysis: Organizations can evaluate the sentiment of stakeholders during project meetings. By understanding stakeholders' sentiments, project managers can adjust their communication strategies to better align with expectations and improve relationships.
- Market Research Insights: Companies can utilize sentiment analysis of industry-related discussion transcripts to monitor trends and sentiments influencing their market. Insights gained can help inform strategic decisions and competitive positioning in the market landscape.