Identify video comment sentiment
using AI
Below is a free classifier to identify video comment sentiment. Just input your text, and our AI will predict the sentiment of video comments - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("video-comment-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/video-comment-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/video-comment-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 video comments.
This pretrained text model uses a Nyckel-created dataset and has 20 labels, including Apologetic, Critical, Disappointed, Dismissive, Encouraging, Enthusiastic, Excited, Frustrated, Indifferent and Mixed.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of video comments).
Whether you're just curious or building video comment sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify video comment sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Content Moderation: This function can help platforms automatically filter and flag comments that are perceived as harmful or negative. By analyzing user sentiments in comments, administrators can ensure a safer environment for users, reducing the potential for abuse and fostering positive interactions.
- Customer Feedback Analysis: Businesses can utilize this function to analyze video comments for sentiment, helping to gauge viewer reactions to products or services. Insights derived from sentiment analysis can inform product development, marketing strategies, and customer service improvements.
- Influencer Marketing Evaluation: Brands can assess the sentiment of comments on influencer marketing videos to evaluate the effectiveness of their campaigns. By understanding audience sentiment, companies can make informed decisions about future partnerships and marketing tactics.
- Competitive Analysis: Companies can apply sentiment analysis to comments on competitor videos to understand public perception and identify market trends. This intelligence can drive strategic planning, product positioning, and customer outreach.
- Engagement Optimization: Content creators can leverage the sentiment analysis function to fine-tune their video content based on audience reactions. By recognizing what resonates positively or negatively, creators can adapt their style and content strategy to enhance viewer engagement.
- Crisis Management: This function can assist organizations in detecting potential public relations issues by monitoring negative sentiment in comments promptly. Early identification allows businesses to address concerns before they escalate, thereby preserving their brand reputation.
- Community Building: Organizations can use sentiment analysis to foster a positive community environment by highlighting and responding to positive comments. Engaging with fans who express positive sentiments can encourage loyalty and build a stronger brand community.