It would have done everything necessary in this scenario, except the source I was trying to scrape captured click events on links (I KNOW!), which tripped things up. Web Scraper is a Chrome plugin that allows you to build navigable site maps and extract elements from those site maps. None of those passed the CAPTCHA test, so I’ll focus on their handling of navigation and extraction. Here are some of the tools that came highly recommended, and my experience with them. The cleanest example of this that I’ve seen is captured in a video for one of the tools below: the interface lets you click on some text you want to pluck out of a website, asks you to label it, and then allows you to correct mistakes it learns how to extract the other examples on the page.Īs you’ll see in a moment, the steps at the top of this list are hardest to automate. On any page you’d like to extract content from, the scraper has to help you identify the data you’d like to extract. Some of the tools I looked at allowed entering text into search boxes, but none of them correctly handled pagination across multiple pages of results.Įxtract the data. This might require entering some text into a search box (e.g., searching for a product on Amazon), or it might require clicking “next” through all of the pages that results are split over (often called pagination). It suggests that as a low bar, good scrapers should facilitate a human in the loop: automate the things machines are good at automating, and fall back to a human to perform authentication tasks the machines can’t do on their own. Because the source I wanted to scrape required filling out a CAPTCHA, all of the automated tools I’ll review below failed step 1. This might require logging in to a website or filling out a CAPTCHA to prove you’re not…a web scraper. There are three steps to a structured extraction pipeline:Īuthenticate yourself. To understand why, I’ll break down the steps required for a working web scraper, and then use those steps to explain where various solutions broke down. The bad news is that none of the tools I tested would work out of the box for the specific use case I was testing. The list comes from memory, the advice of some friends that have done this before, and, most productively, a question on Twitter that Hilary Mason was nice enough to retweet. David Huynh, my wonderful colleague from grad school, prototyped a tool called Sifter that did most of what one would need, but like all good research from 2006, the lasting impact is his paper rather than his software artifact.īelow, I’ve compiled a list of some available tools. I had the pleasure of working on one such project, called Needlebase at ITA before Google acquired it and closed things down. Lots of companies have tried, to varying degrees of success, to build a programmer-free interface for structured web data extraction. But what can one accomplish without writing code? If we want the data on a webpage, we can automate form submissions and key presses, and we can parse some ugly HTML before emitting reasonably structured CSVs or JSON. Amazon has a structured database of their products somewhere, but all you get to see is a bunch of webpages trapped behind some forms.Ī developer usually isn’t hindered by the deep web. Amazon is a nice example of a deep website, where you have to enter text into a search box, click on a few buttons to narrow down your results, and finally access relatively structured data (prices, model numbers, etc.) embedded in HTML. While many public datasources are available in structured form, some sources are hidden in what us data folks call the deep web. They don’t have developers in-house, but feel pretty comfortable analyzing datasets in CSV form. I recently spoke with a resource-limited organization that is investigating government corruption and wants to access various public datasets to monitor politicians and law firms.
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