Data Scraping Services with Proxy Data Scraping

Have you ever heard of "data scraping? Data Scraping is the process of gathering relevant information in the public domain on the internet (private areas even if the conditions are met) and stored in databases or spreadsheets for later use in various applications. Scraping data technology is not new and a successful businessman his fortune by using data scraping technology.

Sometimes owners of sites that are not derived much pleasure from the automated harvesting of their data. Webmasters have learned to deny access to web scrapers their websites using tools or methods that some IP addresses to block the content of the site here. scrapers data is left to either target a different site, or the script to move the harvest of a computer using a different IP address each time and get as much information as possible to "all computers finally blocked the nozzle.

Fortunately, there is a modern solution to this problem. Proxy data scraping technology solves the problem by using a proxy IP addresses. When your data scraping program performs an extraction of a website, the site thinks that it comes from a different IP address. For site owner, proxies just like scratching a short period of increased traffic around the world. They have very limited resources and tedious to block such a scenario, but more importantly - for the most part, they simply do not know they are scraped.

Now you can ask. "Where can I proxy data scraping technology for my project" The "do-it-yourself solution is free, unfortunately, not easy at all Creation of a database scraping proxy network takes time and requires you to either a group of IP addresses and servers can be used in place yet, the computer guru you need to call to get everything configured. You may consider hiring proxy servers hosting providers to select, but this option is usually quite expensive, but probably better than the alternative: dangerous and unreliable servers (but free) public proxy.

There are literally thousands of free proxy servers located all over the world are fairly easy to use. The trick is to find them. Hundreds of sites, list servers, but by placing a functioning, open and supports standard protocols that you need to a lesson in perseverance, trial and error will be. However, if you manage to find a working public representatives, there are dangers inherent in their use. First, you do not know who owns the server or activities taking place elsewhere on the server. Send applications or sensitive data via an open proxy is a bad idea. It's easy enough for a proxy server to keep all information you send or send it back to you to catch. If you choose the method of replacing the public, make sure you never a transaction through which you or anyone else would jeopardize the case of unsavory types are made aware of the data to send.

A less risky scenario for data scraping proxy is to hire a proxy connection that runs through the rotation of a large number of private IP addresses. There are a number of these companies available that claim to remove all Web logs, which you harvest anonymously on the web with a minimal threat of retaliation. Companies such as enterprise solutions offer a large http://www.Anonymizer.com anonymous proxy, but often carry significant costs of installing enough for you to continue.

The other advantage is that companies that own such networks can often help design and implement a set of proxy data scraping custom program instead of trying to work with a generic bone scraping. After performing a simple Google search, I quickly found a company (www.ScrapeGoat.com) that an anonymous proxy server provides for data scraping purposes. Or, according to their website, if you want to make life even easier, scrap goat can retrieve data for you and a variety of different formats to deliver, often before you could finish up your plate from the scraping program.

Whatever path you choose for your data scraping proxy need not let a few simple tips to thwart access to all the wonderful information that is stored on the World Wide Web!

Source:http://www.articlesbase.com/small-business-articles/data-scraping-services-with-proxy-data-scraping-4697825.html

How to scrape address from Google Maps

If you want to build a new online directory based website and want it to be popular with latest web contents, then you need the help of web scraping services from iWeb scraping. If you want to scrape address from maps.google.com, there is a specialized web scraping tool developed by iWeb scraping which can do the job for you. There are plenty of benefits with web scraping which includes market research, gathering customer information, managing product catalogs, compare prices, gather real estate data, gather job posting information etc. Web scraping technology is very popular nowadays and it saves lot of time and effort involved in manual extraction of data from websites.

The web scraping tools developed iWeb Scraping is very user-friendly and can extract specific information from targeted websites. It converts data from HTML web pages to useful formats like Excel spread sheets or Access database. Whatever web scraping requirements you have, you can contact iWeb Scraping as they have more than 3.5 years of web data extraction experience and offer the best prices in the industry. Also their services are available in 24x7 basis and free pilot projects will be done based on request.

Companies which require specific web data and look for an application which can automate the process and export the HTML data in structured format could benefit greatly from web scraping applications of iWeb scraping. You can easily extract data from multiple target websites, parse and re-assemble the information in HTML format to database or spread sheets as you wish. The application has simple point-and-click user-interface and any beginner can use it scrape address from Google Maps. If you want to gather address of people in particular region from Google maps, you can do it with help of web scraping application developed by iWebscraping.

Web Scraping is a technology that able to digest target website databases that are visible only as HTML web pages, and create a local, identical replica of those databases as a information or result. With our web scraping & web data extraction service we can capture web pages, then pin-point specific pieces of data/information you'd like to extract from web pages. What is needed in this process is much more than a Website crawler and set of Website wrappers. The time required to do web data extraction goes down in comparison to manually data copying and pasting job.

Source:http://www.articlesbase.com/information-technology-articles/how-to-scrape-address-from-google-maps-4683906.html

Limitations and Challenges in Effective Web Data Mining

Web data mining and data collection is critical process for many business and market research firms today. Conventional Web data mining techniques involve search engines like Google, Yahoo, AOL, etc and keyword, directory and topic-based searches. Since the Web's existing structure cannot provide high-quality, definite and intelligent information, systematic web data mining may help you get desired business intelligence and relevant data.

Factors that affect the effectiveness of keyword-based searches include:

• Use of general or broad keywords on search engines result in millions of web pages, many of which are totally irrelevant.

• Similar or multi-variant keyword semantics my return ambiguous results. For an instant word panther could be an animal, sports accessory or movie name.

• It is quite possible that you may miss many highly relevant web pages that do not directly include the searched keyword.

The most important factor that prohibits deep web access is the effectiveness of search engine crawlers. Modern search engine crawlers or bot can not access the entire web due to bandwidth limitations. There are thousands of internet databases that can offer high-quality, editor scanned and well-maintained information, but are not accessed by the crawlers.

Almost all search engines have limited options for keyword query combination. For example Google and Yahoo provide option like phrase match or exact match to limit search results. It demands for more efforts and time to get most relevant information. Since human behavior and choices change over time, a web page needs to be updated more frequently to reflect these trends. Also, there is limited space for multi-dimensional web data mining since existing information search rely heavily on keyword-based indices, not the real data.

Above mentioned limitations and challenges have resulted in a quest for efficiently and effectively discover and use Web resources. Send us any of your queries regarding Web Data mining processes to explore the topic in more detail.

Source: http://ezinearticles.com/?Limitations-and-Challenges-in-Effective-Web-Data-Mining&id=5012994

Affordable Tooth Extractions

In recent times, the cost of dental care has skyrocketed. This includes all types of dentistry including teeth cleaning, extractions, and dental surgery. For those who live in Denver, CO, there are many options to choose from when paying for routine or emergency dental care. In fact, having a tooth extraction Denver might just be more easily afforded than what some may be aware of.

The flat fee for a tooth extraction in Denver may vary between dental offices. The type of extraction can also cause a difference in the price. A simple extraction may cost between $60-$75, but a wisdom tooth extraction that requires more time and effort could cost much more.

One of the great aspects of having dental services performed in Denver is the variety of payment forms that many dental offices accept. Most dental offices in this area accept several different health insurance plans that will allow patients to only be required to pay a small copay at the time of service. If you have chosen an in-network dental provider for your plan, this copay can be even less.

Many dental offices also provide services to those who have state medicaid or medicare as well. While cosmetic dental work may not be covered by these forms of health care, extractions are covered because they are considered a necessary part of the patients good health. Yearly checkups and teeth cleanings are also normally covered as a preventative measure to avoid bad dental health.

For those who may not have any type of health insurance, dental insurance, or state provided health care plan, most dental offices will offer a payment plan. The total cost will be calculated and can be divided up over a few months to make dental care more easily affordable. This will need to be arranged before services and you may need to pay a percentage of the cost upfront before any dental work is performed.

So, if you live in the Denver area and need to have a tooth extraction or other dental care, do not fear that it is impossible to obtain. By calling each dental office and discussing the types of payment forms they accept, you may find a payment plan that fits your budget nicely. You can compare the prices and options of all dentists in your area so that you can make a well informed decision more easily.

Source:http://ezinearticles.com/?Affordable-Tooth-Extractions&id=3241427

Benefits of Predictive Analytics and Data Mining Services

Predictive Analytics is the process of dealing with variety of data and apply various mathematical formulas to discover the best decision for a given situation. Predictive analytics gives your company a competitive edge and can be used to improve ROI substantially. It is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.

Predictive analytics can be helpful in answering questions like:

•    Who are most likely to respond to your offer?
•    Who are most likely to ignore?
•    Who are most likely to discontinue your service?
•    How much a consumer will spend on your product?
•    Which transaction is a fraud?
•    Which insurance claim is a fraudulent?
•    What resource should I dedicate at a given time?

Benefits of Data mining include:

•    Better understanding of customer behavior propels better decision
•    Profitable customers can be spotted fast and served accordingly
•    Generate more business by reaching hidden markets
•    Target your Marketing message more effectively
•    Helps in minimizing risk and improves ROI.
•    Improve profitability by detecting abnormal patterns in sales, claims, transactions etc
•    Improved customer service and confidence
•    Significant reduction in Direct Marketing expenses

Basic steps of Predictive Analytics are as follows:

•    Spot the business problem or goal
•    Explore various data sources such as transaction history, user demography, catalog details, etc)
•    Extract different data patterns from the above data
•    Build a sample model based on data & problem
•    Classify data, find valuable factors, generate new variables
•    Construct a Predictive model using sample
•    Validate and Deploy this Model

Standard techniques used for it are:

•    Decision Tree
•    Multi-purpose Scaling
•    Linear Regressions
•    Logistic Regressions
•    Factor Analytics
•    Genetic Algorithms
•    Cluster Analytics
•    Product Association

Should you have any queries regarding Data Mining or Predictive Analytics applications, please feel free to contact us. We would be pleased to answer each of your queries in detail.

Source:http://ezinearticles.com/?Benefits-of-Predictive-Analytics-and-Data-Mining-Services&id=4766989

Handling exceptions in scrapers

When requesting and parsing data from a source with unknown properties and random behavior (in other words, scraping), I expect all kinds of bizarrities to occur. Managing exceptions is particularly helpful in such cases.

Here is some ways that an exception might be raised.
[][0] #The list has no zeroth element, so this raises an IndexError
{}['foo'] #The dictionary has no foo element, so this raises a KeyError

Catching the exception is sometimes cleaner than preventing it from happening in the first place. Here are some examples handling bizarre exceptions in scrapers.

Example 1: Inconsistant date formats

Let’s say we’re parsing dates.
import datetime
This doesn’t raise an error.
datetime.datetime.strptime('2012-04-19', '%Y-%m-%d')
But this does.
datetime.datetime.strptime('April 19, 2012', '%Y-%m-%d')

It raises a ValueError because the date formats don’t match. So what do we do if we’re scraping a data source with multiple date formats?

Ignoring unexpected date formats

A simple thing is to ignore the date formats that we didn’t expect.

import lxml.html
import datetime
def parse_date1(source):
    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text
    try:
         cleandate = datetime.datetime.strptime(rawdate, '%Y-%m-%d')
    except ValueError:
         cleandate = None
    return cleandate

print parse_date1('<div id="date">2012-04-19</div>')

If we make a clean date column in a database and put this in there, we’ll have some rows with dates and some rows with nulls. If there are only a few nulls, we might just parse those by hand.

Trying multiple date formats

Maybe we have determined that this particular data source uses three different date formats. We can try all three.

import lxml.html
import datetime

def parse_date2(source):

    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text

    for date_format in ['%Y-%m-%d', '%B %d, %Y', '%d %B, %Y']:

        try:
             cleandate = datetime.datetime.strptime(rawdate, date_format)
             return cleandate
        except ValueError:
             pass
    return None

print parse_date2('<div id="date">19 April, 2012</div>')

This loops through three different date formats and returns the first one that doesn’t raise the error.

Example 2: Unreliable HTTP connection

If you’re scraping an unreliable website or you are behind an unreliable internet connection, you may sometimes get HTTPErrors or URLErrors for valid URLs. Trying again later might help.

import urllib2
def load(url):
    retries = 3
    for i in range(retries):
        try:
            handle = urllib2.urlopen(url)
            return handle.read()
        except urllib2.URLError:
            if i + 1 == retries:
                raise
            else:
                time.sleep(42)
    # never get here

print load('http://thomaslevine.com')

This function tries to download the page thee times. On the first two fails, it waits 42 seconds and tries again. On the third failure, it raises the error. On a success, it returs the content of the page.

Example 3: Logging errors rather than raising them

For more complicated parses, you might find loads of errors popping up in weird places, so you might want to go through all of the documents before deciding which to fix first or whether to do some of them manually.

import scraperwiki
for document_name in document_names:
    try:
        parse_document(document_name)
    except Exception as e:
        scraperwiki.sqlite.save([], {
            'documentName': document_name,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')

This catches any exception raised by a particular document, stores it in the database and then continues with the next document. Looking at the database afterwards, you might notice some trends in the errors that you can easily fix and some others where you might hard-code the correct parse.

Example 4: Exiting gracefully

When I’m scraping over 9000 pages and my script fails on page 8765, I like to be able to resume where I left off. I can often figure out where I left off based on the previous row that I saved to a database or file, but sometimes I can’t, particularly when I don’t have a unique index.


for bar in bars:
    try:
        foo(bar)
    except:
        print('Failure at bar = "%s"' % bar)
        raise

This will tell me which bar I left off on. It’s fancier if I save the information to the database, so here is how I might do that with ScraperWiki.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except:
        scraperwiki.sqlite.save_var('resume_index', i)
        raise
scraperwiki.sqlite.save_var('resume_index', 0)

ScraperWiki has a limit on CPU time, so an error that often concerns me is the scraperwiki.CPUTimeExceededError. This error is raised after the script has used 80 seconds of CPU time; if you catch the exception, you have two CPU seconds to clean up. You might want to handle this error differently from other errors.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except scraperwiki.CPUTimeExceededError:
        scraperwiki.sqlite.save_var('resume_index', i)
    except Exception as e:
        scraperwiki.sqlite.save_var('resume_index', i)
        scraperwiki.sqlite.save([], {
            'bar': bar,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')
scraperwiki.sqlite.save_var('resume_index', 0)

tl;dr

Expect exceptions to occur when you are scraping a randomly unreliable website with randomly inconsistent content, and consider handling them in ways that allow the script to keep running when one document of interest is bizarrely formatted or not available.

Source: https://blog.scraperwiki.com/2012/05/handling-exceptions-in-scrapers

A quick guide on web scraping: Why and how

Web scraping, which is the collection and cleaning of online data, is the first step in any
data-driven project. Here’s a short video that explains what scraping is, and how to create
automated scraping jobs using a digital tool.

This is a 15-minute video created by an instructor at Ohio State University. In the first six
minutes, the instructor talks about why we need web scraping; he then shows how to use a
scraping tool, OutWit Hub, to collect data scattered in a large database.

FYI: read reviews by Reporters’ Lab of OutWit Hub and other web scraping tools.

Source: http://www.mulinblog.com/quick-guide-web-scraping/

Web scraping tutorial

There are three ways to access a website data. One is through a browser, the other is using a API (if the site provides one) and the last by parsing the web pages through code. The last one also known as Web Scraping is a technique of extracting information from websites using specially coded programs.

In this post we will take a quick look at writing a simple scraperusing the simplehtmldom library. But before we continue a word of caution:

Writing screen scrapers and spiders that consume large amounts of bandwidth, guess passwords, grab information from a site and use it somewhere else may well be a violation of someone’s rights and will eventually land you in trouble. Before writing  a screen scraper first see if the website offers an RSS feed or an API for the data you are looking. If not and you have to use a scraper, first check the websites policies regarding automated tools before proceeding.

Now that we have got all the legalities out of the way, lets start with the examples.

1. Installing simplehtmldom.

Simplehtmldom is a PHP library that facilitates the process of creating web scrapers. It is a HTML DOM parser written in PHP5 that let you manipulate HTML in a quick and easy way. It is a wonderful library that does away with the messy details of regular expressions and uses CSS selector style DOM access like those found in jQuery.

First download the library from sourceforge.  Unzip the library in you PHP includes directory or a directory where you will be testing the code.

Writing our first scraper.

Now that we are ready with the tools, lets write our first web scraper. For our initial idea let us see how to grab the sponsored links section from a google search page.

There are three ways to access a website data. One is through a browser, the other is using a API (if the site provides one) and the last by parsing the web pages through code. The last one also known as Web Scraping is a technique of extracting information from websites using specially coded programs.

In this post we will take a quick look at writing a simple scraperusing the simplehtmldom library. But before we continue a word of caution:

Writing screen scrapers and spiders that consume large amounts of bandwidth, guess passwords, grab information from a site and use it somewhere else may well be a violation of someone’s rights and will eventually land you in trouble. Before writing  a screen scraper first see if the website offers an RSS feed or an API for the data you are looking. If not and you have to use a scraper, first check the websites policies regarding automated tools before proceeding.

Source: http://www.codediesel.com/php/web-scraping-in-php-tutorial/

Web Scraping’s 2013 Review – part 1

Here we are, almost having ended another year and having the chance to analyze the aspects of the Web scraping market over the last twelve months. First of all i want to underline all the buzzwords on the tech field as published in the Yahoo’s year in review article . According to Yahoo, the most searched items wore

•     iPhone (including 4, 5, 5s, 5c, and 6)
•     Samsung (including Galaxy, S4, S3, Note)
•     Siri
•     iPad Cases
•     Snapchat
•     Google Glass
•     Apple iPad
•     BlackBerry Z10
•     Cloud Computing

It’s easy to see that none of this terms regards in any way with the field of data mining, and they rather focus on the gadgets and apps industry, which is just one of the ways technology can evolve to. Regarding actual data mining industry there were a lot of talks about it in this year’s MIT’s Engaging Data 2013 Conference. One of the speakers Noam Chomsky gave an acid speech relating data extraction and its connection to the Big Data phenomena that is also on everyone’s lips this year. He defined a good way to see if Big Data works by following a series of few simple factors: 1. It’s the analysis, not the raw data, that counts. 2. A picture is worth a thousand words 3. Make a big data portal (Not sure if Facebook is planning on dominating in cloud services some day) 4. Use a hybrid organizational model (We’re asleep already, soon)  let’s move 5. Train employees Other interesting declaration  was given by EETimes saying, “Data science will do more for medicine in the next 10 years than biological science.” which says a lot about the volume of required extracted data.

Because we want to cover as many as possible events about data mining this article will be a two parter, so don’t forget to check our blog tomorrow when the second part of this article will come up!

Source:http://thewebminer.com/blog/2013/12/

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/

How can I circumvent page view limits when scraping web data using Python?

I am using Python to scrape US postal code population data from http:/www.city-data.com, through this directory: http://www.city-data.com/zipDir.html. The specific pages I am trying to scrape are individual postal code pages with URLs like this: http://www.city-data.com/zips/01001.html. All of the individual zip code pages I need to access have this same URL Format, so my script simply does the following for postal_code in range:

    Creates URL given postal code
    Tries to get response from URL
    If (2), Check the HTTP of that URL
    If HTTP is 200, retrieves the HTML and scrapes the data into a list
    If HTTP is not 200, pass and count error (not a valid postal code/URL)
    If no response from URL because of error, pass that postal code and count error
    At end of script, print counter variables and timestamp

The problem is that I run the script and it works fine for ~500 postal codes, then suddenly stops working and returns repeated timeout errors. My suspicion is that the site's server is limiting the page views coming from my IP address, preventing me from completing the amount of scraping that I need to do (all 100,000 potential postal codes).

My question is as follows: Is there a way to confuse the site's server, for example using a proxy of some kind, so that it will not limit my page views and I can scrape all of the data I need?

Thanks for the help! Here is the code:

##POSTAL CODE POPULATION SCRAPER##

import requests

import re

import datetime

def zip_population_scrape():

    """
    This script will scrape population data for postal codes in range
    from city-data.com.
    """
    postal_code_data = [['zip','population']] #list for storing scraped data

    #Counters for keeping track:
    total_scraped = 0
    total_invalid = 0
    errors = 0


    for postal_code in range(1001,5000):

        #This if statement is necessary because the postal code can't start
        #with 0 in order for the for statement to interate successfully
        if postal_code <10000:
            postal_code_string = str(0)+str(postal_code)
        else:
            postal_code_string = str(postal_code)

        #all postal code URLs have the same format on this site
        url = 'http://www.city-data.com/zips/' + postal_code_string + '.html'

        #try to get current URL
        try:
            response = requests.get(url, timeout = 5)
            http = response.status_code

            #print current for logging purposes
            print url +" - HTTP:  " + str(http)

            #if valid webpage:
            if http == 200:

                #save html as text
                html = response.text

                #extra print statement for status updates
                print "HTML ready"

                #try to find two substrings in HTML text
                #add the substring in between them to list w/ postal code
                try:           

                    found = re.search('population in 2011:</b> (.*)<br>', html).group(1)

                    #add to # scraped counter
                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])

                    #print statement for logging
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."
                #if substrings not found, try searching for others
                #and doing the same as above   
                except AttributeError:
                    found = re.search('population in 2010:</b> (.*)<br>', html).group(1)

                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."

            #if http =404, zip is not valid. Add to counter and print log        
            elif http == 404:
                total_invalid +=1

                print postal_code_string + ": Not a valid zip code. " + str(total_invalid) + " total invalid zips."

            #other http codes: add to error counter and print log
            else:
                errors +=1

                print postal_code_string + ": HTTP Code Error. " + str(errors) + " total errors."

        #if get url fails by connnection error, add to error count & pass
        except requests.exceptions.ConnectionError:
            errors +=1
            print postal_code_string + ": Connection Error. " + str(errors) + " total errors."
            pass

        #if get url fails by timeout error, add to error count & pass
        except requests.exceptions.Timeout:
            errors +=1
            print postal_code_string + ": Timeout Error. " + str(errors) + " total errors."
            pass


    #print final log/counter data, along with timestamp finished
    now= datetime.datetime.now()
    print now.strftime("%Y-%m-%d %H:%M")
    print str(total_scraped) + " total zips scraped."
    print str(total_invalid) + " total unavailable zips."
    print str(errors) + " total errors."



Source: http://stackoverflow.com/questions/25452798/how-can-i-circumvent-page-view-limits-when-scraping-web-data-using-python