Identify text message sentiment using AI

Below is a free classifier to identify text message sentiment. Just input your text, and our AI will predict the sentiment of your text message - in just seconds.

text message sentiment identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("text-message-sentiment", "your_text_here", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/text-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/text-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 your text message.

This pretrained text model uses a Nyckel-created dataset and has 26 labels, including Angry, Bored, Confused, Content, Curious, Disappointed, Enthusiastic, Excited, Frustrated and Grateful.

We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of your text message).

Whether you're just curious or building text message sentiment detection into your application, we hope our classifier proves helpful.

Recommended Classifiers

Need to identify text message sentiment at scale?

Get API or Zapier access to this classifier for free. It's perfect for:



  • Customer Feedback Analysis: This use case involves analyzing text messages from customers to gauge sentiment about a product or service. Businesses can use this information to identify areas for improvement and enhance customer satisfaction.

  • Social Media Monitoring: Companies can utilize sentiment analysis on text messages and posts on social media platforms to understand public perception. This allows businesses to respond proactively to negative sentiment and capitalize on positive feedback.

  • Employee Engagement Assessment: By examining internal messaging among employees, organizations can identify overall sentiment regarding workplace culture and job satisfaction. This data can help HR departments address issues and improve employee morale.

  • Brand Reputation Management: Businesses can monitor text messages related to their brand to track sentiment over time. This use case aids in managing brand reputation by allowing companies to address any public relations crises promptly.

  • Market Research Insights: Companies can analyze text messages from surveys or feedback forms to gain insights into consumer preferences. Understanding sentiment helps inform product development and marketing strategies to align with customer desires.

  • Crisis Management: In situations of potential public crises, organizations can analyze text messages to assess sentiment and identify concerns quickly. This allows for timely communication and damage control efforts based on public sentiment.

  • Targeted Marketing Campaigns: By evaluating sentiment in consumer messages, marketers can segment audiences based on their feelings towards specific topics or products. This targeted approach to marketing can lead to more effective campaigns and improved conversion rates.

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