Optimizing Web Data Extraction: Choosing Between Scrapy and ParseHub
In today’s digital landscape, transparency around data privacy is more critical than ever. I’ve developed a website (https://www.privana.org/) dedicated to empowering users by providing clear summaries of privacy policies using large language models (LLMs). The goal is to help visitors understand what data applications collect and potentially sell about them, fostering informed decision-making.
Currently, my process involves manually compiling URLs of privacy policies into a database, which I then feed into an LLM for analysis. While effective, this approach is time-consuming and not scalable, especially as the number of apps proliferates. To improve efficiency, I’m considering automating the URL gathering process through web scraping. The idea is to enable users to rapidly search for any app and receive privacy summaries without manual intervention.
However, I’m grappling with questions about the reliability of web scraping solutions. Specifically, I want to ensure that the scraper can consistently locate and extract the correct privacy policy URLs, even when website structures vary or change.
From my research, two popular tools stand out: Scrapy and ParseHub. Both are robust options—Scrapy is an open-source framework known for flexibility and scalability, ideal for developers comfortable with Python. ParseHub offers a user-friendly interface that can handle complex scraping tasks without extensive programming knowledge.
Are these the best choices for this project, or are there alternative tools that might be better suited? Factors I’m considering include ease of setup, accuracy in URL extraction, maintainability, and handling dynamic website content.
Any insights from experienced web scrapers or suggestions on tools and best practices would be greatly appreciated. My goal is to create a streamlined, reliable process for automatically gathering privacy policy URLs, thereby enhancing the user experience and expanding the platform’s capabilities.
