Web & Social Media Scraping

Web Scraping allows you to automatically collect publicly available feedback from websites such as review platforms, forums, community pages, course ratings, or program review sites. The platform continuously extracts new comments, normalizes the content, and routes it into your selected Feedback Group for analysis.

This feature helps you capture feedback that exists outside your organization, ensuring you never miss critical insights from external channels.

What Web Scraping Collects

Depending on the site structure and content availability, the scraper can collect:

  • Open-text reviews

  • Star or numeric ratings

  • Post titles and comment threads

  • Reviewer metadata (name, profile info when public)

  • Timestamps

  • Course/program/product names

  • Tags or categories from the source page

All collected data is cleaned, standardized, and enriched before being added to your Feedback Group.

How Web Scraping Works

Web Scraping requires validation from the BoundaryAI team to activate while it is still in Beta phase.

1. Provide the details

Enter the precise topic or customer-specific query you want the system to scrape from online sources.

Examples:

  • “Customer complaints about ABC Bank’s mobile login failures”

  • “Feedback on ABC Bank’s new savings account fees on social media”

  • “User discussions about ABC Bank’s credit card approval delays”

  • “Mentions of ABC Bank’s mortgage application portal performance issues”

2. Select a Feedback Group

Choose the Feedback Group where scraped data should be stored. All scraped comments will be analyzed alongside your other feedback.

3. Set Scrape Frequency

Choose how often the platform should check the page for new reviews:

  • Daily

  • Weekly

  • Monthly

Newly detected reviews will be appended automatically.

4. Processing & AI Enrichment

Once scraped, the platform will automatically:

  • Clean and structure the text

  • Apply sentiment analysis

  • Attach Flags and themes

  • Identify topics and issues

  • Add timestamps and source metadata

This makes external reviews fully searchable and comparable to your internal feedback.

Best Practices

  • Review scraped data periodically to ensure accuracy.

  • Combine web-scraped data with internal feedback for richer insights.

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