How to use Web Content Extractor(WCE) as Email Scraper?
Web Content Extractor is a great web scraping software developed by Newprosoft Team. The software has easy to use project wizard to create a scraping configuration and scrape data from websites.
One day I came to see the Visual Email Extractor which is also product of Newprosoft and similar to Web Content Extractor but it’s primary use is to scrape email addresses by crawling websites you feed to the scraper. I had noticed that with the little modification in Web Content Extractor project configuration you can use it same as Visual Email Extractor to extract email addresses.
In this post I will show you what configuration makes the Web Content Extractor to extract email addresses. I still recommend Visual Email Extractor as it has lot more features then extracting email using WCE.
Here are the configuration that makes WCE to Extract Emails.
Step 1 : Open Web Content Extractor and Create New Project and Click on Next.
Step 2: Under Crawling Rules -> Advanced Rules Tab do the following settings
Crawling Level 1 Settings
Follow Links if link text equals: *contact*; *feedback*; *support*; *about* for 'Follow Links if link text equals' text box enter following values: contact; feedback; support; about for 'Do not Follow links if URL contains' text box enter following values: google.; yahoo.; bing; msn.; altavista.; myspace.com; youtube.com; googleusercontent.com; =http; .jpg; .gif; .png; .bmp; .exe; .zip; .pdf; Set 'Maximum Crawling Deapth' to 2 set 'Crawling Order' to Deapth First Crawling Tick mark below below check boxes: ->Follow all internal links
Crawling Level 2 Settings
set 'Follow links if link text equals' to below value *contact*; *feedback*; *support*; *about* set 'Follow links if url contains' text box to below value contact; feedback; support; about set 'DO NOT follow links if url contains' text box to below value =http
Web Content Extractor Settings
Step 3 After doing above settings now click on Next -> in Extraction Pattern window -> Click on Define -> in Web Page Address (URL) give any URL where email is given. and click on + sign right of Date Fields to define scraping pattern.
Now inside HTML Structure selects HTML check box or Body check box which means for each page it will take whole page content to parse data.
Now last settings to extract emails from page using regular expression based email extraction function. Open Predefined Script window and select ‘Extract_Email_Addresses‘ and click on OK. and if you have used page that contains email then in Script Result’ you will be able to see the harvested email.
Email Extraction Script Settings
Hope this will help you to use your Web Content Extractor as a Email Scraper.. Share your view in comment.
KEVAL fell in love with Web Scraping during his graduation and from last 5+ years he and his team providing Web Scraping Service and provide Data to Small and Mid size Companies. Web Scraping service is part of services we offer at Smart WebTech
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Web content extractor как пользоваться
It’s a 21st-century truism that web data touches virtually every aspect of our daily lives. We create, consume, and interact with it while we’re working, shopping, traveling, and relaxing. It’s not surprising that web data makes the difference for companies to innovate and get ahead of their competitors. But how to extract data from a website? And what’s this thing called ‘web scraping’?
Why would you want to extract data from a webpage?
Up-to-date, trustworthy data from other websites is the rocket fuel that can power every organization’s successful growth, including your own.
There are multiple reasons you may want to extract data from the web. You might want to compare the pricing of competitors’ products across popular e-commerce sites. You could be monitoring customer sentiment by trawling for name-checks for your brand – favorable or otherwise – in news articles and blogs. Or you might be gleaning information about a particular industry or market sector to guide critical investment decisions.
A concrete example where being able to extract data from the web increasingly valuable role in the financial services industry is insurance underwriting and credit scoring. There are billions of ‘credit invisibles’ around the world, in both developing and mature markets.
Although these individuals don’t possess a standard credit history, there’s a huge range of ‘alternative data’ sources out there, helping lenders assess risk and potentially take these individuals on as clients. These sources range from debit card transactions and utility payments to survey responses, social media posts on a particular topic, and product reviews. Read our blog that explains how public web data can provide financial services providers with a precise, insightful alternative dataset.
Also in the financial sector, hedge fund managers are turning to alternative data – beyond the scope of conventional sources like company reports and bulletins – to help inform their investment decisions. We’ve blogged recently about the value of web data in this space, and how Zyte can help deliver standards-compliant custom data feeds that complement traditional research methodologies.
What is so important about data?
Data, in short, is the differentiating factor for companies when it comes to understanding customers, knowing what competitors are up to – or making just about any kind of commercial decisions based on hard facts rather than intuition.
The web holds answers to all these questions and countless more. Think of it as the world’s biggest and fastest-growing research library. There are billions of web pages out there. This is where knowing how to extract data comes into play. Unlike a static library, however, many of those pages present a moving target when details like product pricing can change regularly.
Whether you’re a developer or a marketing manager, getting your hands on reliable, timely web data might seem like searching for a needle in a huge, ever-changing digital haystack.
The best way to access high-quality and timely web data is to work with a web data partner like Zyte.
What is web scraping?
So you know your business needs to extract data from the web.
What happens next?
There’s nothing to stop you from collecting data from any website manually by cutting and pasting the relevant bits you need from other websites. But it’s easy to make errors, and it’s going to be fiddly, repetitive, and time-consuming for whoever’s been tasked with the job. And by the time you’ve gathered all the data you need, there’s no guarantee that the price or availability of a particular product hasn’t changed.
For all but the smallest projects, you’ll need to turn to some kind of [automated?] extraction solution. Often referred to as ‘web scraping’, data extraction is the art and science of grabbing relevant web data – may be from a handful of pages, or hundreds of thousands – and serving it up in a neatly organized structure that your business can make sense of.
So how does data extraction work? In a nutshell, it makes use of computers to mimic the actions of human beings when they’re finding specific information on a website, quickly, accurately, and at scale. Webpages are designed primarily for the benefit of humans. They tend to present information in ways that we can easily process, understand, and interact with.
If it’s a product page, for example, the name of a book or a pair of trainers is likely to be shown pretty near the top, with the price nearby and probably with an image of the product too. Along with a host of other clues lurking in the HTML code of that webpage, these visual pointers can help a machine pinpoint the data you’re after with impressive accuracy.
There are various practical ways to attack the challenges faced when you extract data.
The crudest is to make use of the wide range of open-source scraping tools that are out there. In essence, these are chunks of ready-written code that scan the HTML content of a webpage, pull out the bits you need, and file them into some kind of structured output.
Going down the open-source route has the obvious appeal of being ‘free’. But it’s not a task for the faint-hearted, and your own developers will spend a fair amount of time writing scripts and tweaking off-the-shelf code to meet the needs of a specific job.
Step-by-step on how to extract data from a product page
OK – it’s time to put all this web scraping theory into practice so you can extract the data you need.
Here’s a worked example that illustrates the three key steps in a real-world extraction project.
1. Create an extraction script
To keep things simple, we are going to use requests and beautifulsoup libraries to create our script.
As an example, I will be extracting product data from this website: books.toscrape.com
The extraction script will contain two functions:
- A crawler to find product URLs
- A scraper that will actually extract information from a website
Making requests is an important part of the script: both for finding the product URLs and fetching the product HTML files. So first, let’s start off by creating a new class and adding the base URL of the website:
ConEx: Web Content Extractor от Josep Silva
ConEx is a web content extractor that extracts the main content from a webpage.
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