• Setting up conditional checks captures reviews only for products in popular categories like apparel or shoes, reducing the volume of requests.

By saving fare data in a local database, I track trends over time, which is useful for identifying the best booking times.

• Tracking both high-priced and budget items reveals consumer spending patterns, which helps forecast growth areas in different economic segments.

• Node.js’s asynchronous capabilities help manage multiple requests concurrently, reducing overall scraping time.

Saving timestamps with product prices supports trend analysis, which is useful for tracking bulk price fluctuations in wholesale markets.

I organize Q&A data by product, which allows for easy filtering and analysis based on the content of each question and answer.

• Setting up a schedule to capture reviews periodically ensures up-to-date data, as Tmall reviews are often posted daily.

• Parsing JSON responses from network requests helps extract structured data, which is more efficient than scraping HTML.

• Rotating IPs and user-agent strings in each request minimizes detection and helps bypass Yahoo! Shopping Japan’s anti-scraping measures.