text-categorizer

Text Categorizer

A versatile utility for classifying and extracting metadata from various types of text content. This package can identify links, code snippets, currencies, social posts, CSV data, and more, making it a powerful tool for content analysis.

Installation

Install the package using npm:

npm install text-categorizer

Basic Usage

Importing the Classifier

import { TextCategorizer } from "text-categorizer";

Classifying Text Content

const result = TextCategorizer.categorize("your text here");
console.log(result);

Features and Examples

1. Detecting Links

const linkResult = TextCategorizer.categorize("https://www.example.com");
console.log(linkResult);
// Output:
// {
//   type: "link",
//   content: "https://www.example.com",
//   metadata: { links: ["https://www.example.com"] }
// }

2. Detecting Code

const codeResult = TextCategorizer.categorize(function hello() {
  return "world";
});
console.log(codeResult);
// Output:
// {
//   type: "code",
//   content: "function hello() {...}",
//   metadata: { codeLanguage: "javascript" }
// }

3. Detecting Currency

const currencyResult = TextCategorizer.categorize("$1,234.56");
console.log(currencyResult);
// Output:
// {
//   type: "currency",
//   content: "$1,234.56",
//   metadata: { amount: 1234.56, currency: "$" }
// }

4. Detecting Social Posts

const socialResult = TextCategorizer.categorize(
  "Check out #typescript with @johnsmith!"
);
console.log(socialResult);
// Output:
// {
//   type: "social",
//   content: "Check out #typescript with @johnsmith!",
//   metadata: {
//     hashtags: ["#typescript"],
//     mentions: ["@johnsmith"]
//   }
// }

5. Detecting CSV

const csvResult = TextCategorizer.categorize(
  "name,age,city\nJohn,30,New York\nJane,25,Boston"
);
console.log(csvResult);
// Output:
// {
//   type: "csv",
//   content: "name,age,city...",
//   metadata: {
//     structured: [
//       { name: "John", age: "30", city: "New York" },
//       { name: "Jane", age: "25", city: "Boston" }
//     ]
//   }
// }

6. Error Handling

try {
  const result = TextCategorizer.categorize("");
  console.log(result);
} catch (error) {
  console.error("Failed to categorize content:", error);
}

7. Type Checking

import { TextCategorizer, ContentCategory } from "text-categorizer";

function processContent(content: string): void {
  const result: ContentCategory = TextCategorizer.categorize(content);

  switch (result.type) {
    case "link":
      console.log("Found links:", result.metadata?.links);
      break;
    case "code":
      console.log("Code language:", result.metadata?.codeLanguage);
      break;
    case "currency":
      console.log("Amount:", result.metadata?.amount);
      console.log("Currency:", result.metadata?.currency);
      break;
    // Handle other types...
  }
}

8. Processing Multiple Items

const contents = [
  "https://example.com",
  "$123.45",
  "const x = 42;",
  "#coding @typescript"
];

contents.forEach((content) => {
  const result = TextCategorizer.categorize(content);
  console.log(`Content type: ${result.type}`);
  console.log("Metadata:", result.metadata);
});

Supported Content Types

The classifier supports the following content types:

• Links
• Code
• Lists
• Email
• JSON
• Markdown
• Equations
• Dates
• Social posts
• Search queries
• Phone numbers
• Addresses
• CSV data
• XML content
• SQL queries
• File paths
• Currency amounts
• Product codes

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests on the GitHub repository.

License

This project is licensed under the MIT License.

Cloning and Running Locally

If you want to clone the repository and run it on your machine, you can do so with the following commands:

1. Clone the repository:

   git clone https://github.com/usmangurowa/text-categorizer.git
   

2. Change directory:

   cd text-categorizer
   

3. Install dependencies:

   npm install
   # or
   yarn install
   

4. Build the application:

   npm run build
   # or
   yarn build
   

5. Run tests:

   npm test
   # or
   yarn test
   

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Enjoy using text-categorizer!