Why scraping and why TheWebMiner?

If you read this blog you are one of two things: you are either interested in web scraping and you have studied this domain for quite a while, or you are just curious about this relatively new field of interest and want to know what it is, how it’s done and especially why. Either way it’s fine!

In case you haven’t googled already this I can tell you that data extraction (or scraping) is a technique in which a computer program extracts data from human-readable output coming from another program (wikipedia). Basically it can collect all the information on a certain subject from certain places. It’s sort of the equivalent of ctrl+f, at the scale of the whole internet. It’s nothing like the search engines that we currently use because it can extract the data in a certain file, as excel, csv (coma separated values) or any other that the buyer wants, and also extracts only the relevant data, only the values that you are interested in.

I hope now that you understand the concept and you are wondering just why would you need such data. Well let’s take the example of an online store, pretty common nowadays, and of course the manager just like any manager wants his business to thrive, so, for that he has to keep up with the other online stores. Now the web scraping takes place: it is very useful for him to have, saved as excels all the competitor’s prices of certain products if not all of them. By this he can maintain a fair pricing policy and always be ahead of his competitors by knowing all of their prices and fluctuations.  Of course the data collecting can also be done manually but this is not effective because we are talking of thousand of products each one having its own page and so on. This is only one example of situation in which scrapping is useful but there are hundreds and each one of them it’s profitable for the company.

By now I’ve talked about what it is and why you should be interested in it, from now on I’m going to explain why you should use thewebminer.com. First of all, it’s easy: you only have to specify what type of data you want and from where and we’ll manage the rest. Throughout the project you will receive first of all an approximation of price, followed by a time approximation. All the time you will be in contact with us so you can find out at any point what is the state of your project. The pricing policy is reasonable and depends on factors like the project size or complexity. For very big projects a discount may be applicable so the total cost be within reason.

Now I believe that thewebminer.com is able to manage with any kind of situation or requirement from users all over the world and to convince you, free samples are available at any project you may have or any uncertainty or doubt.

Source:http://thewebminer.com/blog/2013/07/

Webscraping using readLines and RCurl

There is a massive amount of data available on the web. Some of it is in the form of precompiled, downloadable datasets which are easy to access. But the majority of online data exists as web content such as blogs, news stories and cooking recipes. With precompiled files, accessing the data is fairly straightforward; just download the file, unzip if necessary, and import into R. For “wild” data however, getting the data into an analyzeable format is more difficult. Accessing online data of this sort is sometimes reffered to as “webscraping”. Two R facilities, readLines() from the base package and getURL() from the RCurl package make this task possible.

readLines

For basic webscraping tasks the readLines() function will usually suffice. readLines() allows simple access to webpage source data on non-secure servers. In its simplest form, readLines() takes a single argument – the URL of the web page to be read:

web_page <- readLines("http://www.interestingwebsite.com")

As an example of a (somewhat) practical use of webscraping, imagine a scenario in which we wanted to know the 10 most frequent posters to the R-help listserve for January 2009. Because the listserve is on a secure site (e.g. it has https:// rather than http:// in the URL) we can't easily access the live version with readLines(). So for this example, I've posted a local copy of the list archives on the this site.

One note, by itself readLines() can only acquire the data. You'll need to use grep(), gsub() or equivalents to parse the data and keep what you need.

# Get the page's source
web_page <- readLines("http://www.programmingr.com/jan09rlist.html")
# Pull out the appropriate line
author_lines <- web_page[grep("<I>", web_page)]
# Delete unwanted characters in the lines we pulled out
authors <- gsub("<I>", "", author_lines, fixed = TRUE)
# Present only the ten most frequent posters
author_counts <- sort(table(authors), decreasing = TRUE)
author_counts[1:10]
[webscrape results]


We can see that Gabor Grothendieck was the most frequent poster to R-help in January 2009.

The RCurl package

To get more advanced http features such as POST capabilities and https access, you'll need to use the RCurl package. To do webscraping tasks with the RCurl package use the getURL() function. After the data has been acquired via getURL(), it needs to be restructured and parsed. The htmlTreeParse() function from the XML package is tailored for just this task. Using getURL() we can access a secure site so we can use the live site as an example this time.

# Install the RCurl package if necessary
install.packages("RCurl", dependencies = TRUE)
library("RCurl")
# Install the XML package if necessary
install.packages("XML", dependencies = TRUE)
library("XML")
# Get first quarter archives
jan09 <- getURL("https://stat.ethz.ch/pipermail/r-help/2009-January/date.html", ssl.verifypeer = FALSE)
jan09_parsed <- htmlTreeParse(jan09)
# Continue on similar to above
...

For basic webscraping tasks readLines() will be enough and avoids over complicating the task. For more difficult procedures or for tasks requiring other http features getURL() or other functions from the RCurl package may be required. For more information on cURL visit the project page here.

Source: http://www.r-bloggers.com/webscraping-using-readlines-and-rcurl-2/

Screen scrapers: To program or to purchase?

Companies today use screen scraping tools for a variety of purposes, including collecting competitive information, capturing product specs, moving data between legacy and new systems, and keeping inventory or price lists accurate.

Because of their popularity and reputation as being extremely efficient tools for quickly gathering applicable display data, screen scraping tools or browser add-ons are a dime a dozen: some free, some low cost, and some part of a larger solution. Alternatively, you can build your own if you are (or know) a programming whiz. Each tool has its potential pros and cons, however, to keep in mind as you determine which type of tool would best fit your business need.

Program-your-own screen scraper

Pros:

    Using in-house resources doesn't require additional budget

Cons:

    Properly creating scripts to automate screen scraping can take a significant amount of time initially, and continues to take time in order to maintain the process. If, for instance, objects from which you're gathering data move on a web page, the entire process will either need to be re-automated, or someone with programming acumen will have to edit the script every time there is a change.

    It's questionable whether or not this method actually saves time and resources

Free or cheap scrapers

Pros:

    Here again, budget doesn't ever enter the picture, and you can drive the process yourself.

    Some tools take care of at least some of the programming heavy lifting required to screen scrape effectively

Cons:

    Many inexpensive screen scrapers require that you get up to speed on their programming language—a time-consuming process that negates the idea of efficiency that prompted the purchase.

Screen scraping as part of a full automation solution

Pros:

    In the amount of time it takes to perform one data extraction task, you have a completely composed script that the system writes for you

    It's the easiest to use out of all of the options

    Screen scraping is only part of the package; you can leverage automation software to automate nearly any task or process including tasks in Windows, Excel automation, IT processes like uploads, backups, and integrations, and business processes like invoice processing.

    You're likely to get buy-in for other automation projects (and visibility for the efficiency you're introducing to the organization) if you pick a solution with a clear and scalable business purpose, not simply a tool to accomplish a single task.

Cons:

    This option has the highest price tag because of its comprehensive capabilities.

Looking for more information?

Here are some options to dig deeper into screen scraping, and deciding on the right tool for you:

 Watch a couple demos of what screen scraping looks like with an automation solution driving the process.

 Read our web data extraction guide for a complete overview.

 Try screen scraping today by downloading a free trial.

Source: https://www.automationanywhere.com/screen-scrapers

A Content Marketer's Guide to Data Scraping

As digital marketers, big data should be what we use to inform a lot of the decisions we make. Using intelligence to understand what works within your industry is absolutely crucial within content campaigns, but it blows my mind to know that so many businesses aren't focusing on it.

One reason I often hear from businesses is that they don't have the budget to invest in complex and expensive tools that can feed in reams of data to them. That said, you don't always need to invest in expensive tools to gather valuable intelligence — this is where data scraping comes in.

Just so you understand, here's a very brief overview of what data scraping is from Wikipedia:

    "Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program."

Essentially, it involves crawling through a web page and gathering nuggets of information that you can use for your analysis. For example, you could search through a site like Search Engine Land and scrape the author names of each of the posts that have been published, and then you could correlate this to social share data to find who the top performing authors are on that website.

Hopefully, you can start to see how this data can be valuable. What's more, it doesn't require any coding knowledge — if you're able to follow my simple instructions, you can start gathering information that will inform your content campaigns. I've recently used this research to help me get a post published on the front page of BuzzFeed, getting viewed over 100,000 times and channeling a huge amount of traffic through to my blog.

Disclaimer: One thing that I really need to stress before you read on is the fact that scraping a website may breach its terms of service. You should ensure that this isn't the case before carrying out any scraping activities. For example, Twitter completely prohibits the scraping of information on their site. This is from their Terms of Service:

    "crawling the Services is permissible if done in accordance with the provisions of the robots.txt file, however, scraping the Services without the prior consent of Twitter is expressly prohibited"

Google similarly forbids the scraping of content from their web properties:

    Google's Terms of Service do not allow the sending of automated queries of any sort to our system without express permission in advance from Google.

So be careful, kids.
Content analysis

Mastering the basics of data scraping will open up a whole new world of possibilities for content analysis. I'd advise any content marketer (or at least a member of their team) to get clued up on this.

Before I get started on the specific examples, you'll need to ensure that you have Microsoft Excel on your computer (everyone should have Excel!) and also the SEO Tools plugin for Excel (free download here). I put together a full tutorial on using the SEO tools plugin that you may also be interested in.

Alongside this, you'll want a web crawling tool like Screaming Frog's SEO Spider or Xenu Link Sleuth (both have free options). Once you've got these set up, you'll be able to do everything that I outline below.

So here are some ways in which you can use scraping to analyse content and how this can be applied into your content marketing campaigns:

1. Finding the different authors of a blog

Analysing big publications and blogs to find who the influential authors are can give you some really valuable data. Once you have a list of all the authors on a blog, you can find out which of those have created content that has performed well on social media, had a lot of engagement within the comments and also gather extra stats around their social following, etc.

I use this information on a daily basis to build relationships with influential writers and get my content placed on top tier websites. Here's how you can do it:

Step 1: Gather a list of the URLs from the domain you're analysing using Screaming Frog's SEO Spider. Simply add the root domain into Screaming Frog's interface and hit start (if you haven't used this tool before, you can check out my tutorial here).

Once the tool has finished gathering all the URLs (this can take a little while for big websites), simply export them all to an Excel spreadsheet.

Step 2: Open up Google Chrome and navigate to one of the article pages of the domain you're analysing and find where they mention the author's name (this is usually within an author bio section or underneath the post title). Once you've found this, right-click their name and select inspect element (this will bring up the Chrome developer console).

Within the developer console, the line of code associated to the author's name that you selected will be highlighted (see the below image). All you need to do now is right-click on the highlighted line of code and press Copy XPath.

For the Search Engine Land website, the following code would be copied:

//*[@id="leftCol"]/div[2]/p/span/a

This may not make any sense to you at this stage, but bear with me and you'll see how it works.

Step 3: Go back to your spreadsheet of URLs and get rid of all the extra information that Screaming Frog gives you, leaving just the list of raw URLs – add these to the first column (column A) of your worksheet.

Step 4: In cell B2, add the following formula:

=XPathOnUrl(A2,"//*[@id='leftCol']/div[2]/p/span/a")

Just to break this formula down for you, the function XPathOnUrl allows you to use the XPath code directly within (this is with the SEO Tools plugin installed; it won't work without this). The first element of the function specifies which URL we are going to scrape. In this instance I've selected cell A2, which contains a URL from the crawl I did within Screaming Frog (alternatively, you could just type the URL, making sure that you wrap it within quotation marks).

Finally, the last part of the function is our XPath code that we gathered. One thing to note is that you have to remove the quotation marks from the code and replace them with apostrophes. In this example, I'm referring to the "leftCol" section, which I've changed to ‘leftCol' — if you don't do this, Excel won't read the formula correctly.

Once you press enter, there may be a couple of seconds delay whilst the SEO Tools plugin crawls the page, then it will return a result. It's worth mentioning that within the example I've given above, we're looking for author names on article pages, so if I try to run this on a URL that isn't an article (e.g. the homepage) I will get an error.

For those interested, the XPath code itself works by starting at the top of the code of the URL specified and following the instructions outlined to find on-page elements and return results. So, for the following code:

//*[@id='leftCol']/div[2]/p/span/a

We're telling it to look for any element (//*) that has an id of leftCol (@id='leftCol') and then go down to the second div tag after this (div[2]), followed by a p tag, a span tag and finally, an a tag (/p/span/a). The result returned should be the text within this a tag.

Don't worry if you don't understand this, but if you do, it will help you to create your own XPath. For example, if you wanted to grab the output of an a tag that has rel=author attached to it (another great way of finding page authors), then you could use some XPath that looked a little something like this:

//a[@rel='author']

As a full formula within Excel it would look something like this:

=XPathOnUrl(A2,"//a[@rel='author']")

Once you've created the formula, you can drag it down and apply it to a large number of URLs all at once. This is a huge time-saver as you'd have to manually go through each website and copy/paste each author to get the same results without scraping – I don't need to explain how long this would take.

Now that I've explained the basics, I'll show you some other ways in which scraping can be used…

2. Finding extra details around page authors

So, we've found a list of author names, which is great, but to really get some more insight into the authors we will need more data. Again, this can often be scraped from the website you're analysing.

Most blogs/publications that list the names of the article author will actually have individual author pages. Again, using Search Engine Land as an example, if you click my name at the top of this post you will be taken to a page that has more details on me, including my Twitter profile, Google+ profile and LinkedIn profile. This is the kind of data that I'd want to gather because it gives me a point of contact for the author I'm looking to get in touch with.

Here's how you can do it.

Step 1: First we need to get the author profile URLs so that we can scrape the extra details off of them. To do this, you can use the same approach to find the author's name, with just a little addition to the formula:

=XPathOnUrl(A2,"//a[@rel='author']", <strong>"href"</strong>)

The addition of the "href" part of the formula will extract the output of the href attribute of the atag. In Lehman terms, it will find the hyperlink attached to the author name and return that URL as a result.

Step 2: Now that we have the author profile page URLs, you can go on and gather the social media profiles. Instead of scraping the article URLs, we'll be using the profile URLs.

So, like last time, we need to find the XPath code to gather the Twitter, Google+ and LinkedIn links. To do this, open up Google Chrome and navigate to one of the author profile pages, right-click on the Twitter link and select Inspect Element.

Once you've done this, hover over the highlighted line of code within Chrome's developer tools, right-click and select Copy XPath.

Step 3: Finally, open up your Excel spreadsheet and add in the following formula (using the XPath that you've copied over):

=XPathOnUrl(C2,"//*[@id='leftCol']/div[2]/p/a[2]", "href")

Remember that this is the code for scraping Search Engine Land, so if you're doing this on a different website, it will almost certainly be different. One important thing to highlight here is that I've selected cell C2 here, which contains the URL of the author profile page and not just the article page. As well as this, you'll notice that I've included "href" at the end because we want the actual Twitter profile URL and not just the words ‘Twitter'.

You can now repeat this same process to get the Google+ and LinkedIn profile URLs and add it to your spreadsheet. Hopefully you're starting to see the value in this, and how it can be used to gather a lot of intelligence that can be used for all kinds of online activity, not least your SEO and social media campaigns.

3. Gathering the follower counts across social networks

Now that we have the author's social media accounts, it makes sense to get their follower counts so that they can be ranked based on influence within the spreadsheet.

Here are the final XPath formulae that you can plug straight into Excel for each network to get their follower counts. All you'll need to do is replace the text INSERT SOCIAL PROFILE URL with the cell reference to the Google+/LinkedIn URL:

Google+:

=XPathOnUrl(<strong>INSERTGOOGLEPROFILEURL</strong>,"//span[@class='BOfSxb']")

LinkedIn:

=XPathOnUrl(<strong>INSERTLINKEDINURL</strong>,"//dd[@class='overview-connections']/p/strong")

4. Scraping page titles

Once you've got a list of URLs, you're going to want to get an idea of what the content is actually about. Using this quick bit of XPath against any URL will display the title of the page:

=XPathOnUrl(A2,"//title")

To be fair, if you're using the SEO Tools plugin for Excel then you can just use the built-in feature to scrape page titles, but it's always handy to know how to do it manually!

A nice extra touch for analysis is to look at the number of words used within the page titles. To do this, use the following formula:

=CountWords(A2)

From this you can get an understanding of what the optimum title length of a post within a website is. This is really handy if you're pitching an article to a specific publication. If you make the post the best possible fit for the site and back up your decisions with historical data, you stand a much better chance of success.

Taking this a step further, you can gather the social shares for each URL using the following functions:

Twitter:

=TwitterCount(<strong>INSERTURLHERE</strong>)

Facebook:

=FacebookLikes(<strong>INSERTURLHERE</strong>)

Google+:

=GooglePlusCount(<strong>INSERTURLHERE</strong>)

Note: You can also use a tool like URL Profiler to pull in this data, which is much better for large data sets. The tool also helps you to gather large chunks of data from other social networks, link data sources like Ahrefs, Majestic SEO and Moz, which is awesome.

If you want to get even more social stats then you can use the SharedCount API, and this is how you go about doing it…

Firstly, create a new column in your Excel spreadsheet and add the following formula (where A2 is the URL of the webpage you want to gather social stats for):

=CONCATENATE("http://api.sharedcount.com/?url=",A2)

You should now have a cell that contains your webpage URL prefixed with the SharedCount API URL. This is what we will use to gather social stats. Now here's the Excel formula to use for each network (where B2 is the cell that contaiins the formula above):

StumbleUpon:

=JsonPathOnUrl(B2,"StumbleUpon")

Reddit:

=JsonPathOnUrl(B2,"Reddit")

Delicious:

=JsonPathOnUrl(B2,"Delicious")

Digg:

=JsonPathOnUrl(B2,"Diggs")

Pinterest:

=JsonPathOnUrl(B2,"Pinterest")

LinkedIn:

=JsonPathOnUrl(B2,"Linkedin")

Facebook Shares:

=JsonPathOnUrl(B2,"Facebook.share_count")

Facebook Comments:

=JsonPathOnUrl(B2,"Facebook.comment_count")

Once you have this data, you can start looking much deeper into the elements of a successful post. Here's an example of a chart that I created around a large sample of articles that I analysed within Upworthy.com.

The chart looks at the average number of social shares that an article on Upworthy receives vs the number of words within its title. This is invaluable data that can be used across a whole host of different on-page elements to get the perfect article template for the site you're pitching to.

See, big data is useful!

5. Date/time the post was published

Along with analysing the details of headlines that are working within a site, you may want to look at the optimal posting times for best results. This is something that I regularly do within my blogs to ensure that I'm getting the best possible return from the time I spend writing.

Every site is different, which makes it very difficult for an automated, one-size-fits-all tool to gather this information. Some sites will have this data within the <head> section of their webpages, but others will display it directly under the article headline. Again, Search Engine Land is a perfect example of a website doing this…

So here's how you can scrape this information from the articles on Search Engine Land:

=XPathOnUrl(<strong>INSERTARTICLEURL</strong>,"//*[@class='dateline']/text()")

Now you've got the date and time of the post. You may want to trim this down and reformat it for your data analysis, but you've got it all in Excel so that should be pretty easy.

Extra reading

Data scraping is seriously powerful, and once you've had a bit of a play around with it you'll also realise that it's not that complicated. The examples that I've given are just a starting point but once you get your creative head on, you'll soon start to see the opportunities that arise from this intelligence.

Here's some extra reading that you might find useful:

    http://findmyblogway.com/scraping-communities-with-xpath/

    http://builtvisible.com/data-entry-is-a-waste-of-time/

    http://www.seotakeaways.com/data-scraping-guide-for-seo/

    http://okdork.com/2014/04/30/the-step-by-step-guide-to-10x-growth-for-any-blog/

TL;DR

    Start using actual data to inform your content campaigns instead of going on your gut feeling.

    Gather intelligence around specific domains you want to target for content placement and create the perfect post for their audience.

    Get clued up on XPath and JSON through using the SEO Tools plugin for Excel.

    Spend more time analysing what content will get you results as opposed to what sites will give you links!

    Check the website's ToS before scraping.

Source:http://moz.com/blog/a-content-marketers-guide-to-data-scraping

Is It Time to End Screen Scraping?

As the industry works to improve the way online banking information is shared with personal financial management apps, a debate is brewing over whether to end the decades-old practice of screen scraping.

Proponents of the popular method say it is a valuable supplement to direct data feeds that may be incomplete or out-of-date. But screen scraping also raises risk concerns, since like other data collection methods it requires consumers to cough up their banking credentials.

"I have not talked to a bank that hasn't confirmed it's a growing problem in their organization," said Jim Routh, the chairman of the products and services committee at Financial Services Information Sharing and Analysis Center.

Financial institutions worry that data aggregators may not take all the appropriate security precautions. According to the FS-ISAC, an industry organization, startups are entering the aggregation market without making security a higher priority.

Routh, who is Aetna's chief information security officer and a former global head of application and mobile security for JPMorgan Chase, said the upstarts do some things well, but "protecting credentials isn't necessarily high on their priorities." The problem is worsened by data aggregators that collect marketing data, such as the device a consumer is using, to understand their behaviors across channels, he said.

The FS-ISAC has proposed creating a standard application programming interface to share information from bank accounts. The API would serve as the conduit for data when consumers wish to use a web or mobile app to receive push bill reminders, to verify their bank accounts or for numerous other PFM use cases.

The proposed API would also be designed to reduce the storage of financial data. But if the industry embraces the model, it would be harder for aggregators to do screen-scraping.

For years, PFM companies have used this tool to obtain customers' banking account information. With consumers' permission, aggregators log in with the customer's user name and password to grab financial data and use it to populate the mobile or web app of the customer's choice — whether or not the bank supports the technique.

Yodlee, which works with more than 300 banks as well as startups, argues that there is a place and a need for aggregators to collect data through various techniques to provide the best customer experience.

Brian Costello, vice president of operations and security at Yodlee, said his company uses a combination of methods to gather customer account data. If it couldn't get data from a direct feed, it could also screen scrape.

If the industry moved to embracing only one data exchange method, Yodlee could be more vulnerable to the problem of receiving outdated information from the banks.

When a bank changes an annual percentage rate, if it doesn't update the data feed it sends to the aggregator right away, the PFM services that rely on that data will appear stale. (Services like Credit Karma, Mint and Wallaby, for example, rely on aggregation technology to recommend financial products to consumers according to price, among other things.)

Proper maintenance of data feeds, of course, takes time and money — resources many banks are short on. But delays could also result from the bankers' dilemma: On the one hand, they want to let customers aggregate their accounts to gather intelligence on their competitors. On the other hand, they may have reservations about their rivals collecting that same data in the battle for wallet share.

"Banks are under tremendous pressure to retain and obtain more clients," said Costello.

Screen scraping also has maintenance requirements, though. The FS-ISAC white paper draft said the approach "requires some coordination from the FI to allow what appears to be an automated attack against their application. To avoid blocking the aggregator's attempt to screen scrape the financial institution's application with this or other current security controls, a whitelist of aggregator IPs are set up and maintained by the FIs."

Like Costello, Marc West, president of digital channels at Fiserv, said a combination of data collection methods is better than a standard data exchange approach that might fail to extract the necessary information. Any data feed, said West, offers a limited set of data and information, while a scrape can enable a custom data extract.

But Aetna's Routh said moving to a real-time API model would improve a recurring issue caused by screen scraping: customer service hiccups. A consumer may call the company behind the personal financial app when a link to an account is broken. The PFM provider might tell him to call the bank, when the problem could lie with the aggregator not knowing of an update to the bank's code.

"The consumer gets in the middle of a customer service issue that is thorny at best and unsolvable at worst," Routh said. "Unfortunately that happens more frequently than anyone would like to it happen.

The new model, then, is "inevitable" in Routh's point of view because of the risk and economics involved. "This won't happen overnight," he said. "It needs some legs."

Kristin Moyer, a research vice president in industry advisory services and banking and investment services at Gartner, said she expects more banks to embrace APIs as a way to compete in a digital world.

Already financial institutions like Capital One, Agricole Bank and Fidor Bank are piloting and testing the OAuth specification, which lets banks keep ownership of the customer log-in data but requires them to make available an API. (The FS-ISAC is also promoting OAuth 2.0 as a way to strengthen aggregation security.)

"It's something we will see a lot more of in the next two to three years," said Moyer. "It's an exciting time…I think the use of APIs will enable us as an industry [to do things] that we never really imagined possible before."

LESSONS ABROAD

The move away from screen scraping has already happened in some countries that lack a data exchange standard. Regulators in Poland, for example, recently recommended the practice halt. Responding to the guidance, mBank is one of the banks that changed its aggregation roadmap.

The bank, which spun off from BRE Bank, had been piloting a PFM service with friends and family and has now suspended the pilot. It had, however, already made use of aggregation technology so consumers, who weren't customers of the bank, could get loan decisions from mBank within half an episode of "Modern Family." Indeed, the bank would screen scrape consumers' external bank accounts to make a loan decision within five to 15 minutes. Now, loan decisions have to be made at a branch or for a smaller dollar amount after a consumer sends the bank a copy of an electronic statement.

"Right now we have to put it on the shelf. We haven't killed it. We want to resurrect it," said Michal Panowicz, senior director at mBank.

Overall, he sounds calm about the setback. "This is a regulator decision," said Panowicz. "We have to respect that. …We have to live with them on good footing."

But that doesn't mean it has given up on aggregation. Payday lenders can continue to screen scrape financial data in order to make loan decisions in Poland — which makes it an uneven playing field.

"We will try to convey the logic that [screen scraping] cannot be stopped," said Panowicz.

He views it as a longer term game for something he believes is valuable to consumers. mBank like other banks wants to realize the true aggregation dream: letting customers quickly switch bank accounts and products if they wish.

"To be honest, it's the most exciting part about aggregation... to move accounts to us without spending a minute of physical labor," he said.

Source:http://www.americanbanker.com/news/technology/is-it-time-to-end-screen-scraping-1071118-1.html

Data Scraping Guide for SEO & Analytics

Data scraping can help you a lot in competitive analysis as well as pulling out data from your client’s website like extracting the titles, keywords and content categories.

You can quickly get an idea of which keywords are driving traffic to a website, which content categories are attracting links and user engagement, what kind of resources will it take to rank your site…………and the list goes on…

 Scraping Organic Search Results

By scraping organic search results you can quickly find out your SEO competitors for a particular search term. You can determine the title tags and the keywords they are targeting.

    The easiest way to scrape organic search results is by using the SERPs Redux bookmarklet.

For e.g if you scrape organic listings for the search term ‘seo tools’ using this bookmarklet, you may see the following results:

You can copy paste the websites URLs and title tags easily into your spreadsheet from the text boxes.

    Pro Tip by Tahir Fayyaz:

    Just wanted to add a tip for people using the SERPs Redux bookmarklet.

    If you have a data separated over multiple pages that you want to scrape you can use AutoPager for Firefox or Chrome to loads x amount of pages all on one page and then scrape it all using the bookmarklet.

Scraping on page elements from a web document

Through this Excel Plugin by Niels Bosma you can fetch several on-page elements from a URL or list of URLs like:

    Title tag
    Meta description tag
    Meta keywords tag
    Meta robots tag
    H1 tag
    H2 tag
    HTTP Header
    Backlinks
    Facebook likes etc.

Scraping data through Google Docs

Google docs provide a function known as importXML through which you can import data from web documents directly into Google Docs spreadsheet. However to use this function you must be familiar with X-path expressions.

    Syntax: =importXML(URL,X-path-query)

    url=> URL of the web page from which you want to import the data.

    x-path-query => A query language used to extract data from web pages.

You need to understand following things about X-path in order to use importXML function:

1. Xpath terminology- What are nodes and kind of nodes like element nodes, attribute nodes etc.

2. Relationship between nodes- How different nodes are related to each other. Like parent node, child node, siblings etc.

3. Selecting nodes- The node is selected by following a path known as the path expression.

4. Predicates – They are used to find a specific node or a node that contains a specific value. They are always embedded in square brackets.

If you follow the x-path tutorial then it should not take you more than an hour to understand how X path expressions works.

Understanding path expressions is easy but building them is not. That’s is why i use a firefbug extension named ‘X-Pather‘ to quickly generate path expressions while browsing HTML and XML documents.

Since X-Pather is a firebug extension, it means you first need to install firebug in order to use it.

 How to scrape data using importXML()

Step-1: Install firebug – Through this add on you can edit & monitor CSS, HTML, and JavaScript while you browse.

Step-2: Install X-pather – Through this tool you can generate path expressions while browsing a web document. You can also evaluate path expressions.

Step-3: Go to the web page whose data you want to scrape. Select the type of element you want to scrape. For e.g. if you want to scrape anchor text, then select one anchor text.

Step-4: Right click on the selected text and then select ‘show in Xpather’ from the drop down menu.

Then you will see the Xpather browser from where you can copy the X-path.

Here i have selected the text ‘Google Analytics’, that is why the xpath browser is showing ‘Google Analytics’ in the content section. This is my xpath:

    /html/body/div[@id='page']/div[@id='page-ext']/div[@id='main']/div[@id='main-ext']/div[@id='mask-3']/div[@id='mask-2']/div[@id='mask-1']/div[@id='primary-content']/div/div/div[@id='post-58']/div/ol[2]/li[1]/a

Pretty scary huh. It can be even more scary if you try to build it manually. I want to scrape the name of all the analytic tools from this page: killer seo tools. For this i need to modify the aforesaid path expression into a formula.

This is possible only if i can determine static and variable nodes between two or more path expressions. So i determined the path expression of another element ‘Google Analytics Help center’ (second in the list) through X-pather:

    /html/body/div[@id='page']/div[@id='page-ext']/div[@id='main']/div[@id='main-ext']/div[@id='mask-3']/div[@id='mask-2']/div[@id='mask-1']/div[@id='primary-content']/div/div/div[@id='post-58']/div/ol[2]/li[2]/a

Now we can see that the node which has changed between the original and new path expression is the final ‘li’ element: li[1] to li[2]. So i can come up with following final path expression:

    /html/body/div[@id='page']/div[@id='page-ext']/div[@id='main']/div[@id='main-ext']/div[@id='mask-3']/div[@id='mask-2']/div[@id='mask-1']/div[@id='primary-content']/div/div/div[@id='post-58']/div/ol[2]//li/a

Now all i have to do is copy-paste this final path expression as an argument to the importXML function in Google Docs spreadsheet. Then the function will extract all the names of Google Analytics tool from my killer SEO tools page.

This is how you can scrape data using importXML.

    Pro Tip by Niels Bosma: “Anything you can do with importXML in Google docs you can do with XPathOnUrl directly in Excel.”

    To use XPathOnUrl function you first need to install the Niels Bosma’s Excel plugin. It is not a built in function in excel.

Note:You can also use a free tool named Scrapy for data scraping. It is an an open source web scraping framework and is used to extract structured data from web pages & APIs. You need to know Python (a programming language) in order to use scrapy.

Scraping on-page elements of an entire website

There are two awesome tools which can help you in scraping on-page elements (title tags, meta descriptions, meta keywords etc) of an entire website. One is the evergreen and free Xenu Link Sleuth and the other is the mighty Screaming Frog SEO Spider.

What make these tools amazing is that you can scrape the data of entire website and download it into excel. So if you want to know the keywords used in the title tag on all the web pages of your competitor’s website then you know what you need to do.

Note: Save the Xenu data as a tab separated text file and then open the file in Excel.

 Scraping organic and paid keywords of an entire website

The tool that i use for scraping keywords is SEMRush. Through this awesome tool i can determine which organic and paid keyword are driving traffic to my competitor’s website and then can download the whole list into excel for keyword research. You can get more details about this tool through this post: Scaling Keyword Research & Competitive Analysis to new heights

Scraping keywords from a webpage

Through this excel macro spreadsheet from seogadget you can fetch keywords from the text of a URL(s). However you need an Alchemy API key to use this macro.

You can get the Alchemy API key from here

Scraping keywords data from Google Adwords API

If you have access to Google Adwords API then you can install this plugin from seogadget website. This plugin creates a series of functions designed to fetch keywords data from the Google Adwords API like:

getAdWordAvg()- returns average search volume from the adwords API.

getAdWordStats() – returns local search volume and previous 12 months separated by commas

getAdWordIdeas() – returns keyword suggestions based on API suggest service.

Check out this video to know how this plug-in works

Scraping Google Adwords Ad copies of any website

I use the tool SEMRush to scrape and download the Google Adwords ad copies of my competitors into excel and then mine keywords or just get ad copy ideas.  Go to semrush, type the competitor website URL and then click on ‘Adwords Ad texts’ link on the left hand side menu. Once you see the report you can download it into excel.

Scraping back links of an entire website

The tool that you can use to scrape and download the back links of an entire website is: open site explorer

Scraping Outbound links from web pages

Garrett French of citation Labs has shared an excellent tool: OBL Scraper+Contact Finder which can scrape outbound links and contact details from a URL or URL list. This tool can help you a lot in link building. Check out this video to know more about this awesome tool:

Scraper – Google chrome extension

This chrome extension can scrape data from web pages and export it to Google docs. This tool is simple to use. Select the web page element/node you want to scrape. Then right click on the selected element and select ‘scrape similar’.

Any element/node that’s similar to what you have selected will be scraped by the tool which you can later export to Google Docs. One big advantage of this tool is that it reduces our dependency on building Xpath expressions and make scraping easier.

See how easy it is to scrape name and URLs of all the Analytics tools without using Xpath expressions.

Source: http://www.optimizesmart.com/data-scraping-guide-for-seo/

Screenscraping from Java using jsoup – effective data gathering from websites

In a recent article I discussed screenscraping in a in hindsight fairly clumsy way (http://technology.amis.nl/blog/12786/building-java-object-graph-with-tour-de-france-results-using-screen-scraping-java-util-parser-and-assorted-facilities). While preparing for a series of articles on data visualizations, I had need of statistics regarding the Olympic Games – more specifically: the overall medal count per country during the 2008 Bejing Olympic Games. This information is readily available from dozens of websites. However, I could not find one hat offered the data in easy to process XML or CSV format – all websites had human consumers in mind.

Using screenscraping – we use a programmatic facility to consume the content that is intended to be displayed on screen to human users and subsequently process that content by extracting the required data from it. Some web-pages are easier to scrape than others – this depends on the richness of the HTML (the poorer the better for scraping), the required interactivity (JavaScript, AJAX – the less the better) and the structure used to present the data (tables, frequently despised by web developers, work rather well).

I came across a tool for screenscraping from Java, called jsoup – http://jsoup.org/. It turned out to be so incredibly easy to use – that I thouht I should share it.

Getting going with jsoup is as easy as can be:

1. download jsoup-1.6.1.jar (or whatever the latest version is) from http://jsoup.org/download

2. add this jar as a dependency in your project and/or application CLASSPATH

3. make use of jsoup in the code that does the screenscraping.

A simple example of code that uses jsoup (more examples on: http://jsoup.org/cookbook/):

One of the websites offering the overall medal count is http://www.databaseolympics.com/games/gamesyear.htm?g=26. The page looks as follows:

Image

Well, more importantly, the page looks like this:

Image

This means in terms of screenscraping: I will find the medal count for each country inside a TABLE element with styleclass pt8. Each country has a TR element. Only the first TR element does not represent a country score, as it is the table header. The first TD element in the TR represents the country. The name of the country can be retrieved as the text content from the A element in the TD. The next TD elements contain the numbers of medals in Gold, Silver, Bronze and Total.

The corresponding Java code with jsoup boils down to:

public static void main(String[] args) throws IOException, SQLException, InterruptedException {

        Document doc = Jsoup.connect(OlympicMedalMirrorProcessor.baseUrl + "?g=26").get();
        String title = doc.title();
        System.out.println(title);
        Element table = doc.select("table.pt8").get(0);
        Elements trs = table.select("tr");
        Iterator trIter = trs.iterator();
        boolean firstRow = true;
        while (trIter.hasNext()) {


            Element tr = (Element)trIter.next();
            if (firstRow) {
                firstRow = false;
                continue;
            }
            Elements tds = tr.select("td");
            Iterator tdIter = tds.iterator();
            int tdCount = 1;
            String country = null;
            Integer gold = null;
            Integer silver = null;
            Integer bronze = null;
            Integer total = null;
            // process new line
            while (tdIter.hasNext()) {

                Element td = (Element)tdIter.next();
                switch (tdCount++) {
                case 1:
                    country = td.select("a").text();
                    break;
                case 2:
                    gold = Integer.parseInt(td.text());
                    break;
                case 3:
                    silver = Integer.parseInt(td.text());
                    break;
                case 4:
                    bronze = Integer.parseInt(td.text());
                    break;
                case 5:
                    total = Integer.parseInt(td.text());
                    break;
                }

            }
            System.out.println(country + ": gold " + gold + " silver " + silver + " bronze " + bronze + " total " +
                               total);
        } //table rows

Source:http://technology.amis.nl/2011/08/03/screenscraping-from-java-using-jsoup-effective-data-gathering-from-websites/

Big Data Democratization via Web Scraping

Big Data Democratization via Web Scraping

If  we had to put democratization of data inline with the classroom definition of democracy, it would read- Data by the people, for the people, of the people. Makes a lot of sense, doesn’t it? It resonates with the generic feeling we have these days with respect to easy access to data for our daily tasks. Thanks to the internet revolution, and now the social media.

Big-data-crawling

Big Data web Crawling

By the people- most of the public data on the web is a user group’s sentiments, analyses and other information.

Of the people- Although the “of” here does not literally mean that the data is owned, all such data on the internet either relates to the user group itself or its views on things.

For the people- Most of this data is presented via channels (either social media, news, etc.) for public benefit be it travel tips, daily news feeds, product price comparisons, etc.

Essentially, data democratization has come to mean that by leveraging cloud computing, data that’s mostly user-generated on the internet has become accessible by all industries- big or small for their own internal use (commercial or not). This democratization has been put to use for unearthing hidden patterns from big blobs of datasets. Use cases have evolved with the consumer internet landscape and Big Data is now being used for various other means quite unanticipated.

With respect to the democratization, we’ve also heard enough about how data analytics is paving way beyond data analysts within companies and becoming available to even the non-tech-savvies. But did anyone mention DaaS providers who aid in the very first phase of data acquisition? Data scraping or web crawling (whatever your lingo is) has come to become an indivisible part of data democratization, especially when talking large-scale. The first step into bringing the public data to use is acquiring it which is where setting up web crawlers internally or partnering with DaaS providers comes to play. This blog guides towards making a choice. Its not always all the data that companies crunch or should crunch from the web. There’s obviously certain channels that are of more interest to the community than the rest and there lies the barrier- to identify sources of higher ROI and acquire data in a machine-readable format.

DaaS providers usually come to help with the entire data acquisition pipeline- starting from picking the right sources through crawl, extraction, dedup as well as data normalization based on specific requirements. Once the data has been acquired, its most likely published on another channel. Such network effect bolsters the democracy.

Steps in Data Acquisition Pipeline

crawl-extract-norm

Note- PromptCloud only delivers structured data as per the schema provided.

So while democratization may refer to easy access of computing resources in order to draw patterns from Big Data, it could also be analogous to ensuring right data in the right format at right intervals. In fact, DaaS providers have themselves used this democracy to empower it further.

Source:https://www.promptcloud.com/blog/big-data-democratization-using-web-scraping-2/

Why Businesses Need Data Scraping Service?

With the ever-increasing popularity of internet technology there is an abundance of knowledge processing information that can be used as gold if used in a structured format. We all know the importance of information. It has indeed become a valuable commodity and most sought after product for businesses. With widespread competition in businesses there is always a need to strive for better performances.

Taking this into consideration web data scraping service has become an inevitable component of businesses as it is highly useful in getting relevant information which is accurate. In the initial periods data scraping process included copying and pasting data information which was not relevant because it required intensive labor and was very costly. But now with the help of new data scraping tools like Mozenda, it is possible to extract data from websites easily. You can also take the help of data scrapers and data mining experts that scrape the data and automatically keep record of it.

How Professional Data Scraping Companies and Data Mining Experts Device a Solution?

Data Scraping Plan and Solutions

ImageCredit:http://www.loginworks.com/images/newscapingpage/data-as-service-plan.png

Why Data Scraping is Highly Essential for Businesses?

Data scraping is highly essential for every industry especially Hospitality, eCommerce, Research and Development, Healthcare, Financial and data scraping can be useful in marketing industry, real estate industry by scraping properties, agents, sites etc., travel and tourism industry etc. The reason for that is it is one of those industries where there is cut-throat competition and with the help of data scraping tools it is possible to extract useful information pertaining to preferences of customers, their preferred location, strategies of your competitors etc.

It is very important in today’s dynamic business world to understand the requirements of your customers and their preferences. This is because customers are the king of the market they determine the demand. Web data scraping process will help you in getting this vital information. It will help you in making crucial decisions which are highly critical for the success of business. With the help of data scraping tools you can automate the data scraping process which can result in increased productivity and accuracy.

Reasons Why Businesses Opt. For Website Data Scraping Solutions:

Website Scraping
Demand For New Data:

There is an overflowing demand for new data for businesses across the globe. This is due to increase in competition. The more information you have about your products, competitors, market etc. the better are your chances of expanding and persisting in competitive business environment. The manner in which data extraction process is followed is also very important; as mere data collection is useless. Today there is a need for a process through which you can utilize the information for the betterment of the business. This is where data scraping process and data scraping tools come into picture.

ImageCredit:3idatascraping.com
Capitalize On Hot Updates:

Today simple data collection is not enough to sustain in the business world. There is a need for getting up to date information. There are times when you will have the information pertaining to the trends in the market for your business but they would not be updated. During such times you will lose out on critical information. Hence; today in businesses it is a must to have recent information at your disposal.

The more recent update you have pertaining to the services of your business the better it is for your growth and sustenance. We are already seeing lot of innovation happening in the field of businesses hence; it is very important to be on your toes and collect relevant information with the help of data scrapers. With the help of data scrapping tools you can stay abreast with the latest developments in your business albeit; by spending extra money but it is necessary tradeoff in order to grow in your business or be left behind like a laggard.

Analyzing Future Demands:

Foreknowledge about the various major and minor issues of your industry will help you in assessing the future demand of your product / service. With the help of data scraping process; data scrapers can gather information pertaining to possibilities in business or venture you are involved in. You can also remain alert for changes, adjustments, and analysis of all aspects of your products and services.

Appraising Business:

It is very important to regularly analyze and evaluate your businesses. For that you need to evaluate whether the business goals have been met or not. It is important for businesses to know about your own performance. For example; for your businesses if the world market decides to lower the prices in order to grow their customer base you need to be prepared whether you can remain in the industry despite lowering the price. This can be done only with the help of data scraping process and data scraping tools.

Source:http://www.habiledata.com/blog/why-businesses-need-data-scraping-service

Example of Scraping with Selenium WebDriver in C#

In this article I will show you how it is easy to scrape a web site using Selenium WebDriver. I will guide you through a sample project which is written in C# and uses WebDriver in conjunction with the Chrome browser to login on the testing page and scrape the text from the private area of the website.

Downloading the WebDriver

First of all we need to get the latest version of Selenium Client & WebDriver Language Bindings and the Chrome Driver. Of course, you can download WebDriver bindings for any language (Java, C#, Python, Ruby), but within the scope of this sample project I will use the C# binding only. In the same manner, you can use any browser driver, but here I will use Chrome.

After downloading the libraries and the browser driver we need to include them in our Visual

Studio solution:

VS Solution

Creating the scraping program

In order to use the WebDriver in our program we need to add its namespaces:

using OpenQA.Selenium;
using OpenQA.Selenium.Chrome;
using OpenQA.Selenium.Support.UI;

Then, in the main function, we need to initialize the Chrome Driver:

using (var driver = new ChromeDriver())

{

 This piece of code searches for the chromedriver.exe file. If this file is located in a directory different from the directory where our program is executed, then we need to specify explicitly its path in the ChromeDriver constructor.

When an instance of ChromeDriver is created, a new Chrome browser will be started. Now we can control this browser via the driver variable. Let’s navigate to the target URL first:

driver.Navigate().GoToUrl("http://testing-ground.scraping.pro/login");

Then we can find the web page elements needed for us to login in the private area of the website:

var userNameField = driver.FindElementById("usr");
var userPasswordField = driver.FindElementById("pwd");
var loginButton = driver.FindElementByXPath("//input[@value='Login']");

Here we search for user name and password fields and the login button and put them into the corresponding variables. After we have found them, we can type in the user name and the password  and press the login button:

userNameField.SendKeys("admin");
userPasswordField.SendKeys("12345");
loginButton.Click();

At this point the new page will be loaded into the browser, and after it’s done we can scrape the text we need and save it into the file:

var result = driver.FindElementByXPath("//div[@id='case_login']/h3").Text;

File.WriteAllText("result.txt", result);

That’s it! At the end, I’d like to give you a bonus – saving a screenshot of the current page into a file:

driver.GetScreenshot().SaveAsFile(@"screen.png", ImageFormat.Png);

The complete program listing

using System.IO;
using System.Text;
using OpenQA.Selenium;
using OpenQA.Selenium.Chrome;
using OpenQA.Selenium.Support.UI;

namespace WebDriverTest
{
    class Program
    {
        static void Main(string[] args)
        {
            // Initialize the Chrome Driver
            using (var driver = new ChromeDriver())
            {
                // Go to the home page
                driver.Navigate().GoToUrl("http://testing-ground.scraping.pro/login");

                // Get the page elements
                var userNameField = driver.FindElementById("usr");
                var userPasswordField = driver.FindElementById("pwd");
                var loginButton = driver.FindElementByXPath("//input[@value='Login']");

                // Type user name and password
                userNameField.SendKeys("admin");
                userPasswordField.SendKeys("12345");

                // and click the login button
                loginButton.Click();

                // Extract the text and save it into result.txt
                var result = driver.FindElementByXPath("//div[@id='case_login']/h3").Text;
                File.WriteAllText("result.txt", result);

                // Take a screenshot and save it into screen.png
                driver.GetScreenshot().SaveAsFile(@"screen.png", ImageFormat.Png);
            }
        }
    }
}

Also you can download a ready project here.

Conclusion

I hope you are impressed with how easy it is to scrape web pages using the WebDriver. You can naturally press keys and click buttons as you would in working with the browser. You don’t even need to understand what kind of HTTP requests are sent and what cookies are stored; the browser does all this for you. This makes the WebDriver a wonderful tool in the hands of a web scraping specialist.

Source:http://scraping.pro/example-of-scraping-with-selenium-webdriver-in-csharp/

Web Scraping Popularity Soars

The world is stirred because of the ever-growing web scraping success in almost all of its services. Success stories pertaining to the benefits of online data collection in business, research, politics, health, and almost all aspects of human life are endless. With this popularity surge, it has become a hot issue and many are questioning its legality and reliability.

Looking back, this simple harvesting of pertinent data from competitors and the global market in general like anything else started as a non-threatening and advanced form of web research. Eventually, when the benefits begin to manifest and the system improves, many are lured into it that it has become one of the strongest and fastest growing business in the world.

Simple Beginnings

As naturally as a law of life that great things come from small beginnings, data mining was conceived as a process in gaining information, mostly in research. This act of collecting information through the internet was never imagined to be what it has become nowadays.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-popularity-soars/