taobao-product-reviews

To extract Taobao product reviews for sentiment analysis, you can use an automated script that navigates the product page, triggers lazy-loaded reviews, and fetches structured data including reviewer, date, purchased SKU, content, photos, and rating by itemId.

Web scraping handles dynamic loading for Tmall product reviews by automatically navigating and scrolling the product page to trigger lazy-loaded content, then processing pagination to fetch multiple pages of structured review data.

Extracting Taobao and Tmall reviews provides structured data fields including the reviewer name, review date, purchased SKU variant, review text content, photo URLs, and the product rating.

Yes, you can use this data extraction method to monitor rating changes over time by periodically fetching structured review data for a specific itemId, enabling automation workflows for product research and sentiment tracking.

Yes, you need the Taobao or Tmall product page to be loaded first. The extraction process relies on Python scripts under the scripts directory to perform the data retrieval on the active page.

Limitations of scraping Taobao reviews include relying on the product page being fully loaded in a browser environment and depending on page scrolling to trigger lazy-loaded reviews, which may be constrained by platform anti-scraping mechanisms or pagination limits.