Identify direct message sentiment
using AI
Below is a free classifier to identify direct message sentiment. Just input your text, and our AI will predict the sentiment of direct messages. - 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("direct-message-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/direct-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/direct-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 direct messages..
This pretrained text model uses a Nyckel-created dataset and has 20 labels, including Angry, Apologetic, Cheerful, Confused, Content, Curious, Disappointed, Dismissive, Enthusiastic and Excited.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of direct messages.).
Whether you're just curious or building direct message sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify direct message sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Support Analysis: This use case focuses on enhancing customer service interactions by analyzing the sentiment of direct messages received from customers. By identifying positive, negative, or neutral sentiments, companies can prioritize urgent issues and improve response strategies for better customer satisfaction.
- Brand Reputation Monitoring: Businesses can employ sentiment analysis on direct messages about their brand on social media platforms. This enables companies to track public perception in real-time, allowing them to address negative feedback promptly and capitalize on positive sentiments.
- Marketing Campaign Effectiveness: Marketers can use sentiment analysis to gauge audience reactions to direct messages related to promotional campaigns. By understanding how customers feel about specific messages or offers, businesses can adjust their strategies to maximize engagement and sales.
- Product Development Insights: Companies can analyze sentiment from direct messages surrounding product feedback or inquiries. This information aids in identifying key areas for improvement and innovation, leading to product enhancements that align more closely with customer desires.
- Employee Engagement Monitoring: Organizations can assess the sentiment of internal communications, such as messages between team members. By analyzing the morale and feelings expressed in these messages, HR can proactively address issues and improve workplace culture.
- Competitor Analysis: Businesses can monitor sentiments expressed in direct messages that mention their competitors. By understanding how customers feel about competitor offerings, companies can identify strengths and weaknesses, helping to refine their own positioning and strategies.
- Crisis Management: In situations where a company faces public backlash, direct message sentiment analysis can be crucial for rapid response. By understanding the sentiment of messages related to a crisis, businesses can tailor their communication and mitigation strategies to effectively address concerns and restore trust.