Identify chat message sentiment
using AI
Below is a free classifier to identify chat message sentiment. Just input your text, and our AI will predict the sentiment of the chat message - 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("chat-message-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/chat-message-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/chat-message-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 chat message.
This pretrained text model uses a Nyckel-created dataset and has 9 labels, including Indifferent, 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 the chat message).
Whether you're just curious or building chat message sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify chat message sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Support Sentiment Analysis: By implementing a chat message sentiment identifier, customer support teams can assess the emotional tone of customer inquiries in real-time. This allows representatives to prioritize responses based on urgency and emotional state, improving overall customer satisfaction.
- Marketing Campaign Feedback: Businesses can analyze sentiments from customer feedback in chat messages during marketing campaigns. This data helps companies gauge public perception of their campaigns and adjust strategies accordingly to enhance engagement and effectiveness.
- Employee Engagement Monitoring: HR departments can use sentiment analysis on internal chat messages to gauge employee morale and engagement levels. This can provide insights into workplace culture and help identify areas needing intervention or support.
- Social Media Listening: Companies can deploy sentiment analysis to monitor chat messages related to their brand on social media platforms. Insights gathered from this analysis can inform brand reputation management and enable proactive engagement with customers.
- Product Development Insights: Chat message sentiment identifiers can be used to collect feedback about new or existing products. By understanding customer sentiment, product teams can make data-driven decisions about feature enhancements and future developments.
- Crisis Management: During crisis situations, organizations can utilize chat sentiment analysis to quickly identify negative sentiments growing in discussions. This allows them to respond swiftly and appropriately to mitigate potential reputational damage.
- Content Moderation: Businesses can implement sentiment analysis to monitor chat messages for toxic or harmful sentiments. This assists in maintaining a safe community environment and can trigger alerts for human moderators when necessary.