A pretrained if a text contains a negative word classifier that sorts text into one of 2 categories. Use the if a text contains a negative word API immediately, no training required, then adapt it to your own data when you need more.
Drop in some text and get the prediction back. No signup, no setup.
A sample of the 2 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "The text you want to classify"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "The text you want to classify"},
)
print(response.json())Example response
{
"labelName": "Contains Negative Words",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a text contains a negative word categories, served on Nyckel's own infrastructure — your text snippet stays on Nyckel.
Send raw text to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
Businesses can leverage the identifier to analyze customer feedback by detecting negative sentiments within reviews and comments. This helps companies identify areas of improvement and respond promptly to customer concerns, enhancing customer satisfaction and loyalty.
Organizations can use the text classification function to sift through social media posts about their brand and flag those containing negative language. By doing so, they can address public relations issues swiftly and manage their online reputation effectively.
HR departments can apply this tool to analyze internal employee surveys and feedback forms. By identifying negative language, they can uncover underlying issues affecting employee morale and take proactive steps to foster a healthier workplace culture.
E-commerce platforms can implement the identifier to filter and classify product reviews for moderation. This allows them to remove or highlight negative reviews before they impact potential customers’ purchasing decisions and maintain the credibility of their platforms.
Community-driven platforms can utilize the function to scan user-generated content, such as forum posts or comments, for negative language. This enables them to enforce community guidelines and create a more positive and welcoming environment for users.
Financial institutions can apply this identifier to analyze customer communications or complaints for negative sentiment. By recognizing potential issues early on, they can mitigate risks and manage relationships with clients more effectively.
Companies can use this text classification tool to evaluate brand perception by analyzing survey results regarding brand-related topics. Identifying negative language helps them gain insights into public sentiment and adjust their marketing strategies accordingly.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of text samples in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This if a text contains a negative word classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.