The API may impose limits to ensure fair use and prevent abuse. Please refer to the API plans for specific details on limitations.

A Comment Safe API is an application programming interface that uses artificial intelligence and natural language processing techniques to assess the level of toxicity or harmful content in text inputs.

The API analyzes various linguistic and contextual factors to determine the presence and severity of toxicity in text. It leverages machine learning models trained on large datasets to accurately classify and flag toxic or harmful content.

A Comment Safe API can detect various forms of toxicity, including hate speech, profanity, offensive language, cyberbullying, harassment, and other harmful content.

Yes, Comment Safe API support multiple languages, enabling the analysis and detection of toxic content in different linguistic contexts.

The toxicity analysis endpoint returns a JSON object containing a toxicity score and various attributes indicating the levels of different types of harmful content, such as insults, threats, and profanity.

Key fields in the response include "TOXICITY," "INSULT," "THREAT," "SEVERE_TOXICITY," "IDENTITY_ATTACK," and "PROFANITY," each representing the severity of different toxic elements in the analyzed text.

The response data is structured as a JSON object with an "attributes" section detailing toxicity levels and a "languages" section indicating the detected language of the input text.

The endpoint accepts parameters such as "text" (the comment to analyze) and "language" (to specify the language of the text, defaulting to English).

Users can customize requests by specifying the "text" they want to analyze and selecting the "language" parameter to match the language of the input, enhancing accuracy in detection.

The endpoint provides information on the toxicity levels of the input text, including specific metrics for various types of harmful content, allowing for detailed content moderation.

Data accuracy is maintained through the use of advanced machine learning models trained on extensive datasets, ensuring reliable detection of toxic content across multiple languages.

Typical use cases include moderating comments on social media, filtering user-generated content on forums, and monitoring chat conversations in real-time to prevent the spread of toxic language.

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