ReviewGators Blogs

  Data extraction, according to our users, can be a difficult aspect of the systematic review process. Data extraction is the process of locating and extracting data from study reports so that similar data from multiple sources can be analyzed. It's an essential component of any methodical or scientific review process, as it enables you to assemble a large sufficient dataset to (hopefully!) provide meaningful evidence. Create a Plan It's essential to schedule out the process before you start and clearly explain and record your inclusion and exclusion criteria that are likely to grab the information that will help answer your research question, as well as how to set up your data extraction form to capture this information effectively. This can be anything from a simple outline to a published, peer-reviewed protocol; however, you present it, it's important to plan out your process before you start and clearly define and document your inclusion and exclusion criteria that are

   As we know data is the most necessary component for web scraping review data. A review management platform is powered by online reviews, such as a car is powered by energy or gas. While there are various sources of user reviews such as ReviewGator’s Review Scraper API. As a result, we set out to address our own challenges, and we've learned a lot in the process. We began by using these product reviews in our own tool but quickly discovered that we could market this technology so that others could advantage from such an easy API rather than scraping reviews manually. This was a turning point in our capacity to invest more heavily in this product, not only for ourselves but also for paying users. Following are some of the lessons we learned along the journey. API vs Scraping In an ideal society, review data would be accessible using API, however, that's not the case. We employ APIs whenever possible, however, the majority of the 85+ review sites from which we get information d