About the API:
The Profanity Detection API is a powerful tool for identifying and flagging offensive language in user-generated content. It utilizes advanced language understanding technology to accurately detect a wide range of profanities and toxicities, including severe toxicities, obscene texts, insults, threats, and identity-based hate. This API is designed to help organizations and businesses manage user-generated content on their platforms, by identifying and removing potentially harmful language.
The API is easy to use and can be integrated into any application or platform that allows user-generated content. Once integrated, the API can automatically detect and flag offensive language, making it easy for moderators to review and take appropriate action. Additionally, the API can be configured to automatically remove or censor offensive language, providing an additional level of protection for users and organizations.
The Profanity Detection API can also be used to train machine learning models, by providing a large dataset of labeled offensive language. This can help organizations improve the accuracy of their own models for detecting offensive language.
Overall, the Profanity Detection API is a valuable tool for any organization or business that needs to manage user-generated content. It helps to protect users and businesses from the negative effects of offensive language and makes it easy to identify and remove harmful content. With its advanced language understanding technology, it provides a high level of accuracy and reliability, making it a reliable tool for managing user-generated content.
This endpoint detects profanities, toxicities, severe toxicities, obscene texts, insults, threats, and identity hate in a given text.
Profanity Analyzer - Endpoint Features
| Object | Description |
|---|---|
text |
[Required] |
{"semantic_analysis":{"0":{"id_semantic_model":1,"name_semantic_model":"profanity_words","segment":"Cunt"},"1":{"id_semantic_model":2,"name_semantic_model":"toxic","segment":"Cunt"},"2":{"id_semantic_model":4,"name_semantic_model":"obscene","segment":"Cunt"}}}
curl --location --request POST 'https://zylalabs.com/api/1060/profanity+detection+api/930/profanity+analyzer?text=Required' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Profanity Analyzer endpoint returns a JSON object containing the results of the analysis, including identified profanities, toxicities, and other harmful language types present in the input text.
The key fields in the response data typically include "semantic_analysis," which provides detailed insights into the types of harmful language detected, such as profanities, insults, and threats.
The response data is organized in a structured JSON format. It includes nested objects that categorize the identified harmful language, allowing users to easily parse and utilize the information.
The Profanity Analyzer endpoint accepts parameters such as "text" (the input content to analyze) and optional settings for sensitivity levels or specific language filters to customize the analysis.
The data for profanity detection is sourced from a combination of linguistic databases, user-generated content, and machine learning models trained on diverse datasets to ensure comprehensive coverage of offensive language.
Data accuracy is maintained through continuous model training, validation against real-world examples, and regular updates to the underlying language models to adapt to evolving language use and slang.
Typical use cases include moderating user-generated content on social media, filtering offensive language in chatbots, and ensuring respectful communication in gaming communities and customer service interactions.
Users can utilize the returned data by implementing moderation actions based on the identified harmful language, such as flagging content for review, automatically censoring offensive terms, or adjusting user interactions in real-time.
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