Identify story comment sentiment
using AI
Below is a free classifier to identify story comment sentiment. Just input your text, and our AI will predict the sentiment of comments in a story. - 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("story-comment-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/story-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/story-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 comments in a story..
This pretrained text model uses a Nyckel-created dataset and has 20 labels, including Angry, Conflicted, Content, Disappointed, Enthusiastic, Frustrated, Happy, Hopeful, Inspired and Mixed.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of comments in a story.).
Whether you're just curious or building story comment sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify story comment sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Feedback Analysis: By analyzing the sentiment of comments on product-related stories, businesses can gauge customer satisfaction and identify pain points. This can inform product development and marketing strategies.
- Social Media Monitoring: Companies can monitor the sentiment of comments on their social media posts to understand public perception and engagement. This helps in adjusting campaigns and identifying potential PR issues early.
- Content Moderation: Online platforms can utilize sentiment analysis to filter out negative or harmful comments on stories, ensuring a positive user experience. This can enhance community standards and reduce toxicity in discussions.
- Brand Reputation Management: Businesses can track sentiment in comments related to their brand or industry to assess overall reputation. This can help identify emerging trends or crises that require timely responses.
- Market Research Insights: By aggregating sentiment data from comments on industry-related stories, companies can gain insights into consumer preferences and market trends. This information can guide strategic decisions in product development and positioning.
- Customer Support Enhancement: Analyzing comment sentiment can help customer support teams identify frustrated users or common issues, allowing for proactive outreach and resolution. This not only improves customer satisfaction but also fosters brand loyalty.
- Ad Campaign Effectiveness: Businesses can assess the sentiment of comments on stories associated with ad campaigns to evaluate public reception. This data helps in refining marketing messages and targeting to improve campaign ROI.