Identify Tweet sentiment using AI

Below is a free classifier to identify Tweet sentiment. Just input your text, and our AI will predict which emotion it is - in just seconds.

Tweet sentiment identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("tweet-sentiments-identifier", "your_text_here", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/tweet-sentiments-identifier/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/tweet-sentiments-identifier/invoke
                

How this classifier works

To start, input the text that you'd like analyzed. Our AI tool will then predict which emotion it is.

This pretrained text model uses the tweet_eval dataset and has 3 labels, including Negative, Neutral, & Positive.

We'll also show a confidence score (the higher the number, the more confident the AI model is around which emotion it is).

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

Recommended Classifiers

Need to identify Tweet sentiment at scale?

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



  • Brand Management Teams: Monitor brand perception on Twitter by analyzing sentiment trends in tweets mentioning the brand.

  • Public Relations Agencies: Gauge public reaction to news releases or events by assessing sentiment in related tweets.

  • Political Analysts: Understand public opinion on political issues or candidates by evaluating sentiments in tweets.

  • Market Research Firms: Study consumer sentiment towards products based on Twitter discussions.

  • Social Media Managers: Tailor social media strategies by tracking the tone of responses to posts.

  • Customer Service Departments: Improve customer engagement by responding to tweets with negative sentiments more proactively.

  • Mental Health Advocacy: Identify trends in public mental health discourse on Twitter for intervention campaigns.

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