The Text Analyzer endpoint returns structured data that includes indicators of offensive language in the analyzed text. Key outputs include whether the text contains profanity, a severity score, a risk score, flagged categories, the language of the text, and the identified dialect.

The key fields in the response data are `isProfanity` (Boolean), `score` (numeric), `severity` (numeric), `flaggedFor` (array of categories), `language` (string), and `dialect` (string). These fields provide insights into the nature and severity of the detected offensive language.

The response data is organized in a JSON format, with each key representing a specific aspect of the analysis. For example, `isProfanity` indicates if the text is offensive, while `flaggedFor` lists the reasons for flagging, allowing for easy parsing and interpretation by automated systems.

The Text Analyzer endpoint provides information on the presence of offensive language, severity levels, risk scores, categories of flagged content, and the language and dialect of the text. This comprehensive analysis aids in understanding the toxicity of user-generated content.

Users can customize their data requests by adjusting the input text they send to the Text Analyzer endpoint. While the endpoint does not accept additional parameters, the content of the text itself can vary to test different scenarios and analyze various types of language.

Typical use cases for the Text Analyzer data include content moderation for social media platforms, filtering user comments on websites, enhancing chat applications to prevent harassment, and ensuring compliance with community guidelines by identifying toxic language.

Data accuracy is maintained through continuous updates to the underlying language models and regular evaluations against diverse datasets. This ensures that the API can effectively recognize and classify offensive language across different contexts and dialects.

Standard data patterns in the response include a clear indication of whether the text is profane (`isProfanity`), a numeric score reflecting the severity of the language, and a list of categories in `flaggedFor`. Users can expect consistent formatting and structure in the JSON response.

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