About the API:
The Text Category Recognizer API is a machine learning service that helps users automatically classify text into various predefined categories. It can be used to sort and organize large amounts of text data, and can also be used to improve search results and provide more relevant recommendations to users. It is easy to use and integrates seamlessly into existing applications.
Pass the text to analyze and receive a confidence score between the predefined categories. Closer to 1 means that the text is more likely to be related to that category.
Content classification: The API can be used to classify text content into specific categories, such as news articles, blog posts, product descriptions, etc. This can be useful for organizing and sorting large amounts of text data.
Document classification: The API can be used to classify documents, such as resumes, into predefined categories based on their content.
Search improvement: The API can be used to classify search queries and results into relevant categories, which can improve the accuracy and relevance of search results.
Recommendation systems: The API can be used to classify items or content into specific categories, which can be used to provide more relevant recommendations to users.
Spam detection: The API can be used to classify text messages or emails as spam or non-spam, which can be useful for filtering out unwanted or irrelevant content.
Besides the number of API calls per month, there are no other limitations.
Pass the text that you want to recognize its category from.
The API will categorize the text and retrieve the confidence score, where closer to 1 means that the text is more likely to be from that category.
Text Categorization - Endpoint Features
Object | Description |
---|---|
text |
[Required] Text to recognize the category from. |
{"keyword":{"gardening":1,"tip":1,"urban":1,"farming":1,"guide":1,"resource":1,"compost":1,"practice":1},"topic":{"agriculture":0.36,"chemical":0.24,"information":0.24,"human":0.24,"ground":0.24,"goal":0.24,"field":0.24,"equipment":0.24,"employment":0.24,"education":0.24},"version":"7.5.7","author":"twinword inc.","email":"[email protected]","result_code":"200","result_msg":"Success"}
curl --location --request GET 'https://zylalabs.com/api/880/text+category+recognizer+api/667/text+categorization?text=Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information, whether such information is encoded as bits in a computer memory or transcribed in genes and protein structures in a biological cell. An alternate, more succinct definition of computer science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems.' --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 Text Categorization endpoint returns a JSON object containing categorized keywords, topics, and their associated confidence scores. This helps users understand how closely the text aligns with predefined categories.
Key fields in the response include "keyword" (with category keywords and scores), "topic" (with broader topics and scores), "version" (API version), "author" (provider information), and "result_code" and "result_msg" (status of the request).
The response data is structured as a JSON object. It includes nested objects for "keyword" and "topic," each containing category names as keys and their confidence scores as values, allowing for easy parsing and analysis.
The endpoint provides information on categorized keywords and topics related to the input text, along with confidence scores indicating the strength of the categorization, which aids in understanding the text's context.
Users can customize their requests by varying the input text they provide to the endpoint. The API analyzes the content of the text to return relevant categories and scores based on its content.
Typical use cases include content classification for blogs or articles, document classification for resumes, enhancing search results, improving recommendation systems, and filtering spam messages based on content categorization.
Data accuracy is maintained through machine learning algorithms that continuously learn from new data inputs. Regular updates and training on diverse datasets help improve the model's performance and reliability.
Users can expect a structured response with confidence scores typically ranging from 0 to 1 for each category. Higher scores indicate stronger relevance, allowing users to prioritize categories based on their needs.
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To check how many API calls you have left for the current month, refer to the βX-Zyla-API-Calls-Monthly-Remainingβ field in the response header. For example, if your plan allows 1,000 requests per month and you've used 100, this field in the response header will indicate 900 remaining calls.
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