Identify movie review sentiment using AI

Below is a free classifier to identify movie review sentiment. Just input your text, and our AI will predict if it's positive or negative - in just seconds.

movie review sentiment identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("movie-review-sentiment-identifier", "your_text_here", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/movie-review-sentiment-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/movie-review-sentiment-identifier/invoke
                

How this classifier works

To start, input the text that you'd like analyzed. Our AI tool will then predict if it's positive or negative.

This pretrained text model uses the IMBD Dataset dataset and has 2 labels, including Positive or Negative.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if it's positive or negative).

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

Recommended Classifiers

Need to identify movie review sentiment at scale?

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



  • Entertainment industry: Automatically categorize customer feedback on movies to understand viewer sentiment.

  • Social media management: Track and analyze sentiment from user-generated content related to films to gauge public interest.

  • Market research: Gather insights from movie reviews to inform future film projects and marketing strategies.

  • Customer service: Identify negative feedback quickly to address customer concerns and improve service.

  • Content moderation: Screen user comments for inappropriate content or negative sentiment on entertainment platforms.

  • Advertising and promotion: Tailor marketing campaigns based on sentiment analysis to target specific audience preferences.

  • E-commerce: Recommend products or movies to customers based on their sentiment and previous reviews.

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