• By scraping both review text and star ratings, I can run sentiment analysis to identify positive and negative feedback trends.

• Real estate sites often display data via AJAX. I capture these network requests to access details like property value estimates and recent sales history.

• With Node.js’s asynchronous capabilities, run multiple scraping threads, collecting data across various categories like electronics and fashion.

• By capturing JSON responses from network requests, I can bypass the HTML structure, improving efficiency when scraping structured data.

• Randomizing headers and user-agents in requests reduces the chance of FNAC detecting and blocking the scraper.

• By capturing both the list price and sale price, I can analyze discount trends and identify seasonal sales patterns on Mercari.

Using rotating proxies reduces detection risk, as Rakuten’s servers monitor for frequent requests from single IPs.

• By saving reviews in MySQL, I create a database that supports trend analysis, tracking shifts in customer sentiment over time.

• Using headers that mimic common Japanese browsers reduces detection, allowing my Rust scraper to access more data reliably.

• Monitoring seller feedback and ratings shows how quickly sellers are adapting to consumer needs, indicating platform growth quality.

• Exporting data to MongoDB enables fast data retrieval for analyzing high-demand products on JD.com’s various sections.

• Node.js’s asynchronous capabilities allow for concurrent scraping across multiple categories, reducing the overall scraping time.

• Adding timestamps to Q&A data helps track trends in user questions over time, which is useful for market research.

• PHP’s DOMDocument and XPath functions help extract specific review elements, like rating stars or review text, from Tmall’s HTML.