# Build a full-text dataset of CNN business news

**Use case:** 

Collects CNN business articles published in September 2026 from CNN's monthly archive, with the full article text, title, author, publication and update dates and URL. Use the output as a dataset for sentiment analysis, market research or training and testing language models. Change the category and date range to build a dataset for another topic or period.

## Input

```json
{
  "category": "business",
  "startDate": "2026-09-01",
  "endDate": "2026-09-30",
  "maxArticles": 100,
  "concurrency": 5,
  "categoryMode": "archive",
  "archiveMonthLimit": 2
}
```

## Output

```json
{
  "title": {
    "label": "Article Title",
    "format": "string"
  },
  "author": {
    "label": "Author",
    "format": "string"
  },
  "publicationDate": {
    "label": "Published",
    "format": "string"
  },
  "updatedDate": {
    "label": "Updated",
    "format": "string"
  },
  "content": {
    "label": "Content",
    "format": "string"
  },
  "url": {
    "label": "Article URL",
    "format": "string"
  },
  "scrapedAt": {
    "label": "Scraped At",
    "format": "string"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The CNN Article Scraper Actor is ready to run with the inputs already set for "Build a full-text dataset of CNN business news". [Sign up or log in](https://console.apify.com/create-task-from-example/EkEm8QhC0bagaNzQG) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Build a full-text dataset of CNN business news" task

The CNN Article Scraper Actor will start running based on the input automatically. You can also change all the input fields, like news category, start date, and more, to match your specific use case.

### 3. Receive the "Build a full-text dataset of CNN business news" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as article title, author, published, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Build a full-text dataset of CNN business news" task via API](https://apify.com/filip_cicvarek/cnn-article-scraper.md).

## About this Actor

This example demonstrates how to use [CNN Article Scraper](https://apify.com/filip_cicvarek/cnn-article-scraper.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/filip_cicvarek/cnn-article-scraper.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/filip_cicvarek/cnn-article-scraper.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).