Scraping XML Tables with R

A couple of my good friends also recently started a sports analytics blog. We’ve decided to collaborate on a couple of studies revolving around NBA data found at www.basketball-reference.com. This will be the first part of that project!

Data scientists need data. The internet has lots of data. How can I get that data into R? Scrape it!

People have been scraping websites for as long as there have been websites. It’s gotten pretty easy using R/Python/whatever other tool you want to use. This post shows how to use R to scrape the demographic information for all NBA and ABA players listed at www.basketball-reference.com.

Here’s the code:

###### Settings

library(XML)

 ###### URLs

url<-paste0("http://www.basketball-reference.com/players/",letters,"/")

len<-length(url)

 ###### Reading data

tbl<-readHTMLTable(url[1])[[1]]

 for (i in 2:len)

    {tbl<-rbind(tbl,readHTMLTable(url[i])[[1]])}

 ###### Formatting data

colnames(tbl)<-c("Name","StartYear","EndYear","Position","Height","Weight","BirthDate","College")

tbl$BirthDate<-as.Date(tbl$BirthDate[1],format="%B %d, %Y")

Created by Pretty R at inside-R.org

And here’s the result:Result

Source: http://www.r-bloggers.com/scraping-xml-tables-with-r/

Web Scraping Tools for Non-developers

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. They don’t have developers in-house, but feel pretty comfortable analyzing datasets in CSV form. While many public datasources are available in structured form, some sources are hidden in what us data folks call 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. Amazon has a structured database of their products somewhere, but all you get to see is a bunch of webpages trapped behind some forms.

A developer usually isn’t hindered by the deep web. 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. But what can one accomplish without writing code?

This turns out to be a hard problem. Lots of companies have tried, to varying degrees of success, to build a programmer-free interface for structured web data extraction. I had the pleasure of working on one such project, called Needlebase at ITA before Google acquired it and closed things down. 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.

Below, I’ve compiled a list of some available tools. 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.

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. 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 anatomy of a web scraper

There are three steps to a structured extraction pipeline:

    Authenticate yourself. This might require logging in to a website or filling out a CAPTCHA to prove you’re not…a web scraper. Because the source I wanted to scrape required filling out a CAPTCHA, all of the automated tools I’ll review below failed step 1. 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.

    Navigate to the pages with 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). 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.

    Extract the data. On any page you’d like to extract content from, the scraper has to help you identify the data you’d like to extract. 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.

As you’ll see in a moment, the steps at the top of this list are hardest to automate.

What are the tools?

Here are some of the tools that came highly recommended, and my experience with them. None of those passed the CAPTCHA test, so I’ll focus on their handling of navigation and extraction.

    Web Scraper is a Chrome plugin that allows you to build navigable site maps and extract elements from those site maps. 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. You should give it a shot if you’d like to scrape a simpler site, and the youtube video that comes with it helps get around the slightly confusing user interface.

    import.io looks like a clean webpage-to-api story. The service views any webpage as a potential data source to generate an API from. If the page you’re looking at has been scraped before, you can access an API or download some of its data. If the page hasn’t been processed before, import.io walks you through the process of building connectors (for navigation) or extractors (to pull out the data) for the site. Once at the page with the data you want, you can annotate a screenshot of the page with the fields you’d like to extract. After you submit your request, it appears to get queued for extraction. I’m still waiting for the data 24 hours after submitting a request, so I can’t vouch for the quality, but the delay suggests that import.io uses crowd workers to turn your instructions into some sort of semi-automated extraction process, which likely helps improve extraction quality. The site I tried to scrape requires an arcane combination of javascript/POST requests that threw import.io’s connectors for a lo
op, and ultimately made it impossible to tell import.io how to navigate the site. Despite the complications, import.io seems like one of the more polished website-to-data efforts on this list.

    Kimono was one of the most popular suggestions I got, and is quite polished. After installing the Kimono bookmarklet in your browser, you can select elements of the page you wish to extract, and provide some positive/negative examples to train the extractor. This means that unlike import.io, you don’t have to wait to get access to the extracted data. After labeling the data, you can quickly export it as CSV/JSON/a web endpoint. The tool worked seamlessly to extract a feed from the Hackernews front page, but I’d imagine that failures in the automated approach would make me wish I had access to import.io’s crowd workers. The tool would be high on my list except that navigation/pagination is coming soon, and will ultimately cost money.

    Dapper, which is now owned by Yahoo!, provides about the same level of scraping capabilities as Kimono. You can extract content, but like Kimono it’s unclear how to navigate/paginate.

    Google Docs was an unexpected contender. If the data you’re extracting is in an HTML table/RSS Feed/CSV file/XML document on a single webpage with no navigation/authentication, you can use one of the Import* functions in Google Docs. The IMPORTHTML macro worked as advertised in a quick test.

    iMacros is a tool that I could imagine solves all of the tasks I wanted, but costs more than I was willing to pay to write this blog post. Interestingly, the free version handles the steps that the other tools on this list don’t do as well: navigation. Through your browser, iMacros lets you automate filling out forms, clicking on “next” links, etc. To perform extraction, you have to pay at least $495.

    A friend has used Screen-scraper in the past with good outcomes. It handles navigation as well as extraction, but costs money and requires a small amount of programming/tokenization skills.

    Winautomation seems cool, but it’s only available for Windows, which was a dead end for me.

So that’s it? Nothing works?

Not quite. None of these tools solved the problem I had on a very challenging website: the site clearly didn’t want to be crawled given the CAPTCHA, and the javascript-submitted POST requests threw most of the tools that expected navigation through links for a loop. Still, most of the tools I reviewed have snazzy demos, and I was able to use some of them for extracting content from sites that were less challenging than the one I initially intended to scrape.

All hope is not lost, however. Where pure automation fails, a human can step in. Several proposals suggested paying people on oDesk, Mechanical Turk, or CrowdFlower to extract the content with a human touch. This would certainly get us past the CAPTCHA and hard-to-automate navigation. It might get pretty expensive to have humans copy/paste the data for extraction, however. Given that the tools above are good at extracting content from any single page, I suspect there’s room for a human-in-the-loop scraping tool to steal the show: humans can navigate and train the extraction step, and the machine can perform the extraction. I suspect that’s what import.io is up to, and I’m hopeful they keep the tool available to folks like the ones I initially tried to help.

While we’re on the topic of human-powered solutions, it might make sense to hire a developer on oDesk to just implement the scraper for the site this organization was looking at. While a lot of the developer-free tools I mentioned above look promising, there are clearly cases where paying someone for a few hours of script-building just makes sense.

Source: http://blog.marcua.net/post/74655674340

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

Web Scraping for SEO with these Open-Source Scrapers

When conducting Search Engine Optimization (SEO), we’re required to scrape websites for data, our campaigns, and reports for our clients. At the lowest level we utilize scraping to keep track of rankings on search engines like Google, Bing, and Yahoo, even keep a track of links on websites to know when it’s completed its lifespan. Then we’ve used them to help us aggregate data from APIs, RSS feeds, and websites to conduct some of our data mining to find patterns to help us become more competitive. 

So scraping is a function majority of companies (SEOmoz, Raventools, and Google) have to do to either save money, protect intellectual property, track trends, etc… Businesses can find infinite uses with scraping tools, it just depends if you’re an printed circuit board manufacturer looking for ideas on your e-mail marketing campaign or a Orange County based business trying to keep an eye out on the competition. which is why we’ve created a comprehensive list of open source scrapers out there to help all the businesses out there. Just keep in mind we haven’t used all of them!

Words of caution, web scrapers require knowledge specific to the language such as PHP & cURL. Take into considerations issues like cookie management, fault tolerance, organizing the data properly, not crashing the website being scraped, and making sure the website doesn’t prohibit scraping.

If you’re ready, here’s the list…

Erlang

    eBot

Java

    Heritrix
    Nutch
    Piggy Bank
    WebSPHINX
    WebHarvest

PHP

    PHPCrawl
    Snoopy
    SpiderMonkey

Python

    BeautifulSoap
    HarvestMan
    Scrape.py
    Scrapemark
    Scrapy **
    Mechanize

Ruby

    Anemone
    scRUBYt

We’ll come back and update this list as we encounter more! If you would like to submit a solution we missed, feel free. Also we’re looking for guides related to each of these, so if you know of any or would be interested in guesting blogging about one, let us know!

Source:http://www.annexcore.com/blog/web-scraping-for-seo-with-these-open-source-scrapers/

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/

Is Web Scraping Legal?

Web scraping might be one of the best ways to aggregate content from across the internet, but it comes with a caveat: It’s also one of the hardest tools to parse from a legal standpoint.

For the uninitiated, web scraping is a process whereby an automated piece of software extracts data from a website by “scraping” through the site’s many pages. While search engines like Google and Bing do a similar task when they index web pages, scraping engines take the process a step further and convert the information into a format which can be easily transferred over to a database or spreadsheet.

It’s also important to note that a web scraper is not the same as an API. While a company might provide an API to allow other systems to interact with its data, the quality and quantity of data available through APIs is typically lower than what is made available through web scraping. In addition, web scrapers provide more up-to-date information than APIs and are much easier to customize from a structural standpoint.

The applications of this “scraped” information are widespread. A journalist like Nate Silver might use scrapers to monitor baseball statistics and create numerical evidence for a new sports story he’s working on. Similarly, an eCommerce business might bulk scrape product titles, prices, and SKUs from other sites in order to further analyze them.

Legality of Web ScrapingWhile web scraping is an undoubtedly powerful tool, it’s still undergoing growing pains when it comes to legal matters. Because the scraping process appropriates pre-existing content from across the web, there are all kinds of ethical and legal quandaries that confront businesses who hope to do leverage scrapers for their own processes.

In this “wild west” environment, where the legal implications of web scraping are in a constant state of flux, it helps to get a foothold on where the legal needle currently falls. The following timeline outlines some of the biggest cases involving web scrapers in the United States, and allows us to achieve a greater understanding on the precedents that surround the court rulings.

Terms of Use Tug-of-War—2000-2009

For years after they first came into use, web scrapers went largely unchallenged from a legal standpoint. In 2000, however, the use of scrapers came under heavy and consistent fire when eBay fired the first shot against an auction data aggregator called Bidder’s Edge. In this very early case, eBay argued that Bidder’s Edge was using scrapers in a way that violated Trespass to Chattels doctrine. While the lawsuit was settled out of court, the judge upheld eBay’s original injunction, stating that heavy bot traffic could very well disrupt eBay’s service.

Then in 2003’s Intel Corp. v. Hamidi, the California Supreme court overturned the basis of eBay v. Bidder’s Edge, ruling that Trespass to Chattels could not extend to the context of computers if no actual damage to personal property occurred.

So in terms of legal action against web scraping, Tresspass to Chattels no longer applied, and things were back to square one. This began a period in which the courts consistently rejected Terms of Service as a valid means of prohibiting scrapers, including cases like Perfect 10 v. Google, and Cvent v. Eventbrite.

The Takeaway: The earliest cases against scrapers hinged on Trespass to Chattels law, and were successful. However, that doctrine is no longer a valid approach.

Facebook Web Scraping2009—Facebook Steps In

In 2009, Facebook turned the tides of the web scraping war when Power.com, a site which aggregated multiple social networks into one centralized site, included Facebook in their service. Because Power.com was scraping Facebook’s content instead of adhering to their established standards, Facebook sued Power on grounds of copyright infringement.

In denying Power.com’s motion to dismiss the case, the Judge ruled that scraping can constitute copying, however momentary that copying may be. And because Facebook’s Terms of Service don’t allow for scraping, that act of copying constituted an infringement on Facebook’s copyright. With this decision, the waters regarding the legality of web scrapers began to shift in favor of the content creators.

The Takeaway: Even if a web scraper ignores infringing content on its way to freely-usable content, it might qualify as copyright infringement by virtue of having technically “copied” the infringing content first.

2011-2014— U.S. v Auernheimer

In 2010, hacker Andrew “Weev” Auernheimer found a security flaw in AT&T’s website, which would display the email addresses of users who visited the site via their iPads. By exploiting the flaw using some simple scripts and a scraper, Auernheimer was able to gather thousands of emails from the AT&T site.

Although these email addresses were publicly available, Auernheimer’s exploit led to his 2012 conviction, where he was charged with identity fraud and conspiracy to access a computer without authorization.

Data ScrapingEarlier this year, the court vacated Auernheimer’s conviction, ruling that the trial’s New Jersey venue was improper. But even though the case turned out to be mostly inconclusive, the court noted the fact that there was no evidence to show that “any password gate or code-based barrier was breached.” This seems to leave room for the web scraping of publicly-available personal information, although it’s still very much open to interpretation and not set in stone.

The Takeaway: Using a web scraper to aggregate sensitive personal information can lead to a conviction, even if that information was technically available to the public. While there is hope in the court’s observation that no passwords or barriers were broken to retrieve this information, the waters here are still very volatile.

2013—Associated Press vs. Meltwater

Meltwater is a software company whose “Global Media Monitoring” product uses scrapers to aggregate news stories for paying clients. The Associated Press took issue with Meltwater’s scraping of their original stories, some of which had been copyrighted. In 2012, AP filed suit against Meltwater for copy infringement and hot news misappropriation.

While it’s already been established that facts cannot be copyrighted, the court decided that the AP’s copyrighted articles—and more specifically, the way in which the facts within those articles were arranged—were not fair game for copying. On top of this, Meltwater’s use of the articles failed to meet the established fair use standards, and could not be defended on that front either.

The Takeaway: Fair use is limited when it comes to web scrapers, and copyrighted content is not always open to be scraped.

~~

By closely observing the outcomes of previous rulings, you’ll find that there are a few guidelines that a scraper should attempt to adhere to:

    Content being scraped is not copyright protected
    The act of scraping does not burden the services of the site being scraped
    The scraper does not violate the Terms of Use of the site being scraped
    The scraper does not gather sensitive user information
    The scraped content adheres to fair use standards

While all of these guidelines are important to understand before using scrapers, there are other ways to acclimate to the legal nuances. In many cases, you’ll find that a simple conversation with a business software developer or consultant will lead to some satisfying conclusions: Odds are, they’ve used scrapers in the past and can shed light on any snags they’ve hit in the process. And of course, talking with a lawyer is always an ideal course of action when treading into questionable legal territory.

Source:http://blog.icreon.us/2014/09/12/web-scraping-and-you-a-legal-primer-for-one-of-its-most-useful-tools/

The PromptCloud Advantage- Web Scraping with an Edge

The global market is now more aware of its data scraping needs. And so with the demand, the list of suppliers has grown too. This post is dedicated to bringing out the PromptCloud Advantage among such providers.

PromptCloud-Winning-The Race

1. The know-how- Crawling the web, as mundane as it may sound, is a fairly complex task. No one is to be blamed for overlooking the complexity as these things surface only after you’ve tried it yourself and delved into the nitty-gritty. The design decisions you take sit at the core of what you build and eventually monetize. And the long-term effects of such architectural choices are as pleasing if you’ve done it right as disturbing they might turn out if you’re not far-sighted.

Although the expertise of building the tech stack for such large-scale data acquisition, distributing your clusters (and putting thoughts into their geographical locations), maintaining queues, databases and backups, does come from ‘been there done that’, we have been lucky to have the tech advantage imbibed into us since inception. Not that we got it right the first time, but our systems have evolved with technologies, improving each day. Now that we have been there in this business for the last 56 months, it does feel like a long journey for our stack and yes, we do know better :)

2. SLAs- SLAs are what bolsters the data itself. PromptCloud’s key SLAs are scale and quality; while not compromising the data coverage or the politeness policies on your sources. Since we perform focused crawls, there’s no dilution of data and you can consume it all or ask us to index it in order to search using logical combinations in queries. For your reference, here’s a list of all SLAs to visit while picking your data service provider.

changing_place_changing_time_changing_thouts_changing_future.

3. The Experience- There are many scraping tools and crawling services in the market which might just serve the need. What PromptCloud provides is a data acquisition experience; and we go as many number of extra miles as you’d like us to go for it. By leveraging our DaaS platform, we make sure you get what you need from the time you start your research for a data provider through importing the data feeds into your database. We hear your requirements in detail, make sure we’ve got it right by sharing samples and going multiple iterations of reprocessing the data to match your needs while you battle internally on freezing your requirements. But what’s more magical is the way all these feeds get delivered to you, at the intervals you requested; programatically.

It might be evident for the SLAs and the know-how fusing to provide the experience, but it’s that additional human touch that actually aids in sustaining it. We make sure you’re at peace while our systems handle the roadblocks and sort out the messiness on the web.

Source:https://www.promptcloud.com/blog/the-promptcloud-advantage-web-scraping/

Web scraping services-importance of scraped data

Web scraping services are provided by computer software which extracts the required facts from the website. Web scraping services mainly aims at converting unstructured data collected from the websites into structured data which can be stockpiled and scrutinized in a centralized databank. Therefore, web scraping services have a direct influence on the outcome of the reason as to why the data collected in necessary.

It is not very easy to scrap data from different websites due to the terms of service in place. So, the there are some legalities that have been improvised to protect altering the personal information on different websites. These ‘rules’ must be followed to the letter and to some extent have limited web scraping services.

Owing to the high demand for web scraping, various firms have been set up to provide the efficient and reliable guidelines on web scraping services so that the information acquired is correct and conforms to the security requirements. The firms have also improvised different software that makes web scraping services much easier.

Importance of web scraping services

Definitely, web scraping services have gone a long way in provision of very useful information to various organizations. But business companies are the ones that benefit more from web scraping services. Some of the benefits associated with web scraping services are:

    Helps the firms to easily send notifications to their customers including price changes, promotions, introduction of a new product into the market. Etc.
    It enables firms to compare their product prices with those of their competitors
    It helps the meteorologists to monitor weather changes thus being able to focus weather conditions more efficiently
    It also assists researchers with extensive information about peoples’ habits among many others.
    It has also promoted e-commerce and e-banking services where the rates of stock exchange, banks’ interest rates, etc. are updated automatically on the customer’s catalog.

Advantages of web scraping services

The following are some of the advantages of using web scraping services

    Automation of the data

    Web scraping can retrieve both static and dynamic web pages

    Page contents of various websites can be transformed

    It allows formulation of vertical aggregation platforms thus even complicated data can still be extracted from different websites.

    Web scraping programs recognize semantic annotation

    All the required data can be retrieved from their websites

    The data collected is accurate and reliable

Web scraping services mainly aims at collecting, storing and analyzing data. The data analysis is facilitated by various web scrapers that can extract any information and transform it into useful and easy forms to interpret.

Challenges facing web scraping

    High volume of web scraping can cause regulatory damage to the pages

    Scale of measure; the scales of the web scraper can differ with the units of measure of the source file thus making it somewhat hard for the interpretation of the data

    Level of source complexity; if the information being extracted is very complicated, web scraping will also be paralyzed.

It is clear that besides web scraping providing useful data and information, it experiences a number of challenges. The good thing is that the web scraping services providers are always improvising techniques to ensure that the information gathered is accurate, timely, reliable and treated with the highest levels of confidentiality.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/191-web-scraping-services-importance-of-scraped-data/

Review: import.io’s New Scraping Process and Features

Web scraping Data platform import.io, announced last week that they have secured $3M in funding from investors that include the founders of Yahoo! and MySQL.

They also released a new beta version of the tool that is essentially a better version of their extraction tool, with some new features and a much cleaner and faster user experience.

First Impression

I’ve used the tool for a week and can say it is an improvement over the old version – which was a bit bulky and awkward. While still not exactly the most intuitive process, the development team at import.io has managed to slim down what was a relatively button heavy process, without sacrificing any of the functionality – they made the new workflow both simpler and more complicated at the same.

The new version features a simple tool bar across the top as opposed to the space hogging table and wizard from before, which is a large improvement on the pink and white of the previous version.

True, the loss of the wizard means there isn’t as much guidance as before (the pop-up help only appears on the first use), but the undo button means you don’t really need it. You can click around and experiment a bit with the different extraction options before settling down to do some real work.

Data Extraction

Once you’ve figured out how it works, the new version requires far fewer mouse clicks to get from the page to a table of data/API as shown in their homepage video.

All you need to do now is navigate to a website, click a single piece of data on the page – such as price, image, or URL – and their app will find all the other examples of similar data on the website, immediately creating a structured table of data.

download2

This latest version of the extractor also includes a important new feature labeled “Suggest Data”. Its important because it lets you extract all the data from a page, instantly creating a table of data that can be published as an API. This makes import.io very exciting and quick, I spent a long time playing with this and it worked on the majority of sites.

Advanced Features

Most non-programmer web scrapers struggle with complex sites that use JavaScript or iFrames, but import.io also now deals with this. In the basic mode you can toggle JavaScript and CSS on and off to help you see your data better.

If that doesn’t work, you can switch into an ‘advanced mode’ where import allows you to write your own XPath and RegExp. They’ve also added a source code view, though without the ability to click on the site and inspect element (like in Chrome) this feature isn’t particularly useful.

API Integration

Once you’ve created your scraper, there are a number of options for what you can do with it.

If you’ want you can just copy and paste the data into a spreadsheet or Download as CSV. You can also push your data directly Google Sheets, with import.io’s self generated formula.

For the rest of us, they have surfaced both the POST and GET requests for you and given you a JSON view which allows you to see how the data is returned, which is handy.

All this functionality is nice, and it’s clear they’re trying to cater to all technical levels, but it has made the API page somewhat messy and potentially confusing for newer or less technical users, but they should be able to get what they need.

Good with lots of Potential

Their new tool certainly isn’t perfect. There are still a few sites where manual row training is required and you can’t access the authentication feature (though you can still do this in the old version) or pagination.

Even if it’s not quite there yet, if import.io continue like this, they are well on its way to becoming the best data scraping platform on the market. Especially when you consider the “free for life” price tag.

Source:http://scraping.pro/review-import-ios-new-scraping-tools-features/

Web Scraping Enters Politics

Web scraping is becoming an essential tool in gaining an edge over everything about just anything. This is proven by international news on US political campaigns, specifically by identifying wealthy donors. As is commonly known, election campaigns should follow a rule regarding the use of a certain limited amount of money for the expenses of each candidate. Being so, much of the campaign activities must be paid by supporters and sponsors.

It is not a surprise then that even politics is lured to make use of the dynamic and ever growing data mining processes. Once again, web mining has proven to be an essential component of almost all levels of human existence, the society, and the world as a whole. It proves its extraordinary capacity to dig precious information to reach the much aspired for goals of every individual.

Mining for personal information

The CBC News online very recently disclosed that the US Republican presidential candidate Mitt Romney has used data mining in order to identify rich donors. It is reported that the act of getting personal information such as the buying history and church attendance were vital in this incident. Through this information, the party was able to identify prospective rich donors and indeed tap them. As a businessman himself, Romney knows exactly how to fish and where the fat fish are. Moreover, what is unique about the identified donors is that they have never been donating before.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-enters-politics/