• I rotate IPs and vary request timing on each platform to avoid detection and respect rate limits for high-traffic pages.

• I create a data template to standardize common fields, like title, price, and description, across different stores for consistent output.

By scheduling scrapes, I capture new reviews periodically, building a historical dataset for sentiment analysis over time.

• Rust’s Actix framework supports concurrent scraping, allowing for quick collection of multiple routes, such as Tokyo-Osaka or Tokyo-Sapporo.

Storing historical data on product volume helps track the overall growth of each platform’s inventory, which reflects regional market demand.

Monitoring network requests reveals JSON data for product details, allowing faster extraction compared to HTML parsing.

By saving price data with timestamps, I track price trends, which is useful for monitoring flash sales and seasonal discounts on JD.com.

For dynamic elements like stock status, Selenium can fully render pages, allowing for accurate capture of product availability.