Data Discovery vs. Data Extraction

Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.

Source: http://ezinearticles.com/?Data-Discovery-vs.-Data-Extraction&id=165396

Tips for scraping business directories

Tips for scraping business directories

Are you looking to scrape business directories to generate leads?

Here are a few tips for scraping business directories.

Web scraping is not rocket science. But there are good and bad and worst ways of doing it.

Generating sales qualified leads is always a headache. The old school ways are to buy a list from sites like Data.com. But they are quite expensive.

Scraping business directories can help generate sales qualified leads. The following tips can help you scrape data from business directories efficiently.

1) Choose a good framework to write the web scrapers. This can help save a lot of time and trouble. Python Scrapy is our favourite, but there are other non-pythonic frameworks too.

2) The business directories might be having anti-scraping mechanisms. You have to use IP rotating services to do the scrape. Using IP rotating services, crawl with multiple changing IP addresses which can cover your tracks.

3) Some sites really don’t want you to scrape and they will block the bot. In these cases, you may need to disguise your web scraper as a human being. Browser automation tools like selenium can help you do this.

4) Web sites will update their data quite often. The scraper bot should be able to update the data according to the changes. This is a hard task and you need professional services to do that.

One of the easiest ways to generate leads is to scrape from business directories and use enrich them. We made Leadintel for lead research and enrichment.

Source: http://blog.datahut.co/tips-for-scraping-business-directories/

Content Scrapers – How to Find Out Who is Stealing Your Content & What to Do About It

If you have been blogging for a while, chances are you are familiar with content scrapers. Content scrapers are websites that steal your content for their own blogs without your permission. Some content scrapers will just copy the content off of your blog, but most use automated software that takes the content from your RSS feed and posts your content to their site like it is a new post.

In this post, we are going to look at some potential link building benefits to content scrapers, how to find out what sites are scraping your content, and what you can do if you want to either benefit from the linking standpoint or have them take it down.

Linking Benefits of Content Scrapers

Last week, I was happy to see that I was listed in ProBlogger’s 20 Bloggers to Watch in 2012. Within 24 hours, I received a notification in my WordPress dashboard that a page on my blog had been linked to in the post on ProBlogger’s site.

After receiving the original notification from the ProBlogger post, I also received another 18 trackbacks from sites that had stolen the content in their post verbatim. Trackbacks are WordPress’ way of letting you know that another website has linked to a post on your blog. In this case, these 18 sites had posted the content exactly like the original post – with the links back to my blog still intact.

It was then that I started contemplating the potential link building benefits of content scrapers. These are not by any means quality links – the highest Google PageRank was a PR 2 domain, many were stealing content in a variety of languages, and one even had the nerve to use some kind of redirection script to take away the link juice of outgoing links! So while these links didn’t have the same authority that the original post had, they still count as links.

How to Catch Content Scrapers

Unfortunately, unless you want to continuously search for your post titles in Google, you’ll only be able to easily track down sites that keep your in-content links active. If you want to know what websites are scraping your content, here are a few tips to sniff them out.

Copyscape

Copyscape is a simple search engine that allows you to enter the URL of your content to find out if there are duplicates of it on the Internet. You can get a few results using their free search, or you can pay for a premium account to check up to 10,000 pages on your site and more.

Trackbacks

The first way is through your trackbacks in WordPress (as shown in the image above). Many of these will show up in the spam folder if you use Akismet. The key to getting trackbacks to appear from content scrapers is to always include links to other posts in your content. Be sure those links have great anchor text too, if you’re going for a little extra link juice. And even if you are not, internal linking with strong anchor text is good for your on-site optimization too!

Anyone thinking about link building benefits at this point is probably noting the sheer volume of links from these sites, some of which are content scrapers. Essentially any site that is linking to a lot of your posts that isn’t a social network, social bookmarking site, or a die-hard fan who just loves linking to you is potentially a content scraper. You’ll have to go to their website to be sure. To find your links on their site, click on one of the domains to see the details of what pages on your site they are linking to specifically.

You can see here that they are just blatantly copying my posts titles. When I visited one of the links, sure enough, they are copying my entire posts in their full glory onto their site.

Google Alerts

If you don’t post often or want to keep up with any mentions of your top blog posts on other websites, you can create a Google Alert using the exact match for your post’s title by putting the title in quotation marks.

I deliver all of my Google Alerts to an RSS feed so I can manage them in Google Reader, but you can also have them delivered regularly by email. You’ll even get an instant preview of the types of results you will get.

How to Get Credit for Scraped Posts

If you use WordPress, then you definitely want to try out the RSS footer plugin. This plugin allows you to place a custom piece of text at the top or bottom of your RSS feed content.
As you can see, even if you aren’t using it for the purpose of getting credit back to your posts when content thieves steal it, you can still use it for a little extra bit of advertising with the possible benefit of people who subscribe to your RSS feed clicking through to your website or social profiles. And when someone does scrape your content from your RSS feed, it shows up there too

So in the event that someone finds your scraped content, they will hopefully notice the credit before assuming it was created by the blog that stole it. If you don’t have WordPress, you can simply include a note at the top or bottom of your content that includes the same information.

How to Stop Content Scrapers

If you’re not interested in anyone copying your content, then you have a few options to choose from. You can start by contacting the site that is stealing your content and sending them a notice that you want all of your content removed immediately. You can do this through the site’s contact form, email address, or post it to any social accounts they list.

If there is no contact information on the website stealing your content, you can do a Whois Lookup to (hopefully) find out who owns the domain.

If it is not privately registered, you should find an administrative contact’s email address. If not, you should at least see the domain registrar which, in this case, is GoDaddy and/or the hosting company for the website which, in this case, is HostGator. You can try to contact both companies (HostGator has a DMCA form and GoDaddy has an email) and let them know that the domain in question is stealing copyrighted content in hopes that the website will be suspended or removed.

You can also visit the DMCA and use their takedown services to remove anyone who is copying your photos, video, audio, blog, or other content. They even offer a WordPress plugin to incorporate a DMCA protected badge on your site to warn potential thieves.

Have you ever dealt with content scrapers and thieves? Do you leave it alone for the link benefits, or do you fight back? What other tools, services, or other preventative tactics do you use to block content scrapers? Please share your thoughts and experiences in the comments!

Source URL : https://blog.kissmetrics.com/content-scrapers/

Data Scraping – Will Definitely Benefit a Business Startup

With increasingly data shared using internet, the data collected as well as the usage cases are increasing with an unbelievable pace. We’ve entered into the “Big Data” age and data scraping is among the resources to supply big data engines, the latest data for analytical analytics, contest monitoring, or just to steal the data.

From the technology viewpoint, competent data scraping is fairly complicated. It has many open-source projects that allow anybody to run a web data scraper through him. Nevertheless it’s the entire different story while it needs to be an interior of the business as well as that you require not only maintaining your scrapers but also scaling them as well as extract the data smartly as you need.

That is the reason why different services are selling the “data scraping” as service. Their work is taking care about all the technical characteristics so that you can have the data required without any industrial knowledge. Fundamentally all these startups pay attention for collecting the data and then extract its value for selling it to the customers.

Let’s take some examples:

• Sales Intelligence – The scrapers monitor competitors, marketplaces, online directories, and data from the public markets to discover leads. For instance, some tool’s track websites that drop or add JavaScript tags from the competitors therefore you can call them as eligible leads.
• Price Intelligence – A very ordinary use is the price monitoring. If this is in with e-commerce, travel, or property industry monitoring competitors’ prices as well as adjusting yours consequently is generally the key. All these services monitor the prices and using the analytical algorithms they may provide you advice about where the puck can be.
• Marketing – Data scraping may also be used for monitoring how the competitors are doing. From the reviews they have on the marketplaces to get coverage as well as financially published data one can find out a lot. Concerned about marketing, there is a development hacking class which teaches how to use scraping for the marketing objectives.

Finance intelligence, economic intelligence, etc have more and more financial, political, and economical data accessible online with the newer type of services that collect and add up of that, are increasing.

Let’s go through some points concerned with the market:

• It’s tough to evaluate how huge the data scraping market is as this is with the intersection of many big industries like sales, IT security, finance and marketing intelligence. This method is certainly a small part of all these industries however is expected to increase in the coming years.
• It’s a secured bet to indicate that increasingly SaaS will get pioneering applications for the web data scraping as well as progressively startups will use data scraping services from the safety viewpoint.
• As all the startups are generally entering huge markets using niche products / approaches (web data scraping isn’t a solution of everything, it’s more like a feature) they are expected to be obtained by superior players (within the safety, sales, or marketing tools industries). The technological barriers are also there.

Source URL : http://www.3idatascraping.com/data-scraping-will-definitely-benefit-a-business-startup.php

Web Scraping Services : Making Modern File Formats More Accessible

Data scraping is the process of automatically sorting through information contained on the internet inside html, PDF or other documents and collecting relevant information to into databases and spreadsheets for later retrieval. On most websites, the text is easily and accessibly written in the source code but an increasing number of businesses are using Adobe PDF format (Portable Document Format: A format which can be viewed by the free Adobe Acrobat software on almost any operating system. See below for a link.). The advantage of PDF format is that the document looks exactly the same no matter which computer you view it from making it ideal for business forms, specification sheets, etc.; the disadvantage is that the text is converted into an image from which you often cannot easily copy and paste. PDF Scraping is the process of data scraping information contained in PDF files. To PDF scrape a PDF document, you must employ a more diverse set of tools.

There are two main types of PDF files: those built from a text file and those built from an image (likely scanned in). Adobe's own software is capable of PDF scraping from text-based PDF files but special tools are needed for PDF scraping text from image-based PDF files. The primary tool for PDF scraping is the OCR program. OCR, or Optical Character Recognition, programs scan a document for small pictures that they can separate into letters. These pictures are then compared to actual letters and if matches are found, the letters are copied into a file. OCR programs can perform PDF scraping of image-based PDF files quite accurately but they are not perfect.

Once the OCR program or Adobe program has finished PDF scraping a document, you can search through the data to find the parts you are most interested in. This information can then be stored into your favorite database or spreadsheet program. Some PDF scraping programs can sort the data into databases and/or spreadsheets automatically making your job that much easier.

Quite often you will not find a PDF scraping program that will obtain exactly the data you want without customization. Surprisingly a search on Google only turned up one business, that will create a customized PDF scraping utility for your project. A handful of off the shelf utilities claim to be customizable, but seem to require a bit of programming knowledge and time commitment to use effectively. Obtaining the data yourself with one of these tools may be possible but will likely prove quite tedious and time consuming. It may be advisable to contract a company that specializes in PDF scraping to do it for you quickly and professionally.

Let's explore some real world examples of the uses of PDF scraping technology. A group at Cornell University wanted to improve a database of technical documents in PDF format by taking the old PDF file where the links and references were just images of text and changing the links and references into working clickable links thus making the database easy to navigate and cross-reference. They employed a PDF scraping utility to deconstruct the PDF files and figure out where the links were. They then could create a simple script to re-create the PDF files with working links replacing the old text image.

A computer hardware vendor wanted to display specifications data for his hardware on his website. He hired a company to perform PDF scraping of the hardware documentation on the manufacturers' website and save the PDF scraped data into a database he could use to update his webpage automatically.

PDF Scraping is just collecting information that is available on the public internet. PDF Scraping does not violate copyright laws.

PDF Scraping is a great new technology that can significantly reduce your workload if it involves retrieving information from PDF files. Applications exist that can help you with smaller, easier PDF Scraping projects but companies exist that will create custom applications for larger or more intricate PDF Scraping jobs.

Source URL :  http://yellowpagesdatascraping.blogspot.in/2015/06/web-scraping-services-making-modern.html

Scraping the Bottom of the Barrel - The Perils of Online Article Marketing

Many online article marketers so desperately wish to succeed, they want to dump corporate life and work for themselves out of their home. They decide they are going to create an online money making website. Therefore, they look around to see what everyone else is doing, and watch the methods others use to attract online buyers, and then they mimic their marketing, their strategies, and their business models.

Still, if you are copying what other people (less ethical people) are doing in online article marketing, those which are scraping the bottom of the barrel and using false advertising and misrepresentations, then all you are really doing is perpetuating distrust on the Internet. Therefore, you are hurting everyone, including people like me. You must realize that people like me don't appreciate that.

Let me give you a few examples of some of the things going on out there, thing that are being done by people who are ethically challenged. Far too many people write articles and then on their byline they send the Internet surfer or reader of the article to a website that has a squeeze page. The squeeze page has no real information on it, rather it asks for their name and e-mail address.

If the would-be Internet surfer is unwise enough to type in their name and email address they will be spammed by e-mail, receiving various hard-sell marketing pieces. Then, if the Internet Surfer does decide to put in their e-mail address, the website grants them access and then takes them to the page with information about what they are selling, or their online marketing "make you a millionaire" scheme.

Generally, these are five page sales letters, with tons of testimonials of people you've never heard of, and may not actually exist, and all sorts of unsubstantiated earnings claims of how much money you will make if you give them $39.35 by way of PayPal, for this limited offer "Now!" And they will send you an E-book with a strategic plan of how you can duplicate what they are doing. The reality is whatever they are doing is questionable to begin with.

Source URL  : http://ezinearticles.com/?Scraping-the-Bottom-of-the-Barrel---The-Perils-of-Online-Article-Marketing&id=2710103

Beginner’s guide to Web Scraping in Python (using Beautiful Soup)

Introduction

The need and importance of extracting data from the web is becoming increasingly loud and clear. Every few weeks, I find myself in a situation where we need to extract data from the web. For example, last week we were thinking of creating an index of hotness and sentiment about various data science courses available on the internet. This would not only require finding out new courses, but also scrape the web for their reviews and then summarizing them in a few metrics! This is one of the problems / products, whose efficacy depends more on web scrapping and information extraction (data collection) than the techniques used to summarize the data.

Ways to extract information from web

There are several ways to extract information from the web. Use of APIs being probably the best way to extract data from a website. Almost all large websites like Twitter, Facebook, Google, Twitter, StackOverflow provide APIs to access their data in a more structured manner. If you can get what you need through an API, it is almost always preferred approach over web scrapping. This is because if you are getting access to structured data from the provider, why would you want to create an engine to extract the same information.

Sadly, not all websites provide an API. Some do it because they do not want the readers to extract huge information in structured way, while others don’t provide APIs due to lack of technical knowledge. What do you do in these cases? Well, we need to scrape the website to fetch the information.

There might be a few other ways like RSS feeds, but they are limited in their use and hence I am not including them in the discussion here.

What is Web Scraping?

Web scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet).

You can perform web scrapping in various ways, including use of Google Docs to almost every programming language. I would resort to Python because of its ease and rich eocsystem. It has a library known as ‘Beautiful Soup’ which assists this task. In this article, I’ll show you the easiest way to learn web scraping using python programming.

For those of you, who need a non-programming way to extract information out of web pages, you can also look at import.io . It provides a GUI driven interface to perform all basic web scraping operations. The hackers can continue to read this article!

Libraries required for web scraping

As we know, python is a open source programming language. You may find many libraries to perform one function. Hence, it is necessary to find the best to use library. I prefer Beautiful Soup (python library), since it is easy and intuitive to work on. Precisely, I’ll use two Python modules for scraping data:

Urllib2: It is a Python module which can be used for fetching URLs. It defines functions and classes to help with URL actions (basic and digest authentication, redirections, cookies, etc). For more detail refer to the documentation page.

Beautiful Soup: It is an incredible tool for pulling out information from a webpage. You can use it to extract tables, lists, paragraph and you can also put filters to extract information from web pages. In this article, we will use latest version Beautiful Soup 4. You can look at the installation instruction in its documentation page.

Beautiful Soup does not fetch the web page for us. That’s why, I use urllib2 in combination with the BeautifulSoup library.

Python has several other options for HTML scraping in addition to Beatiful Soup. Here are some others:

    -mechanize
    -scrapemark
    -scrapy

Basics – Get familiar with HTML (Tags)

While performing web scarping, we deal with html tags. Thus, we must have good understanding of them.                      
 you already know basics of HTML, you can skip this section. Below is the basic syntax of HTML:
  This syntax has various tags as elaborated below:

    <!DOCTYPE html> : HTML documents must start with a type declaration
      HTML document is contained between <html> and </html>
      The visible part of the HTML document is between <body> and </body>
       HTML headings are defined with the <h1> to <h6> tags
       HTML paragraphs are defined with the <

Scrapping a web Page using Beautiful Soup

Here, I am scraping data from a Wikipedia page. Our final goal is to extract list of state, union territory capitals in India. And some basic detail like establishment, former capital and others form this wikipedia page. Let’s learn with doing this project step wise step:

Import necessary libraries:

#import the library used to query a website
import urllib2
#specify the url
wiki = "https://en.wikipedia.org/wiki/List_of_state_and_union_territory_capitals_in_India"
#Query the website and return the html to the variable 'page'
page = urllib2.urlopen(wiki)
#import the Beautiful soup functions to parse the data returned from the website
from bs4 import Beautiful Soup
#Parse the html in the 'page' variable, and store it in Beautiful Soup format
soup = Beautiful Soup(page)

Use function “prettify” to look at nested structure of HTML page

Above, you can see that structure of the HTML tags. This will help you to know about different available tags and how can you play with these to extract information.

Work with HTML tags

    soup.<tag>: Return content between opening and closing tag including tag.
    In[30]:soup.title
    Out[30]:<title>List of state and union territory capitals in India - Wikipedia, the free encyclopedia</title>
    soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u'List of state and union territory capitals in India - Wikipedia, the free encyclopedia'

Find all the links within page’s <a> tags::  We know that, we can tag a link using tag “<a>”. So, we should go with option soup.a and it should return the links available in the web page. Let’s do it.

    In [40]:soup.a
    Out[40]:<a id="top"></a>

Above, you can see that, we have only one output. Now to extract all the links within <a>, we will use

Above, it is showing all links including titles, links and other information.  Now to show only links, we need to iterate over each a tag and then return the link using attribute “href” with get.

Find the right table: As we are seeking a table to extract information about state capitals, we should identify the right table first. Let’s write the command to extract information within all table tags.

all_tables=soup.find_all('table')

Now to identify the right table, we will use attribute “class” of table and use it to filter the right table. In chrome, you can check the class name by right click on the required table of web page –> Inspect element –> Copy the class name OR go through the output of above command find the class name of right table.

right_table=soup.find('table', class_='wikitable sortable plainrowheaders')

\right_table

Extract the information to DataFrame: Here, we need to iterate through each row (tr) and then assign each element of tr (td) to a variable and append it to a list. Let’s first look at the HTML structure of the table (I am not going to extract information for table heading <th>)
Above, you can notice that second element of <tr> is within tag <th> not <td> so we need to take care for this. Now to access value of each element, we will use “find(text=True)” option with each element.  Let’s look at the code

#Generate lists

A=[]
B=[]
C=[]
D=[]
E=[]
F=[]
G=[]
for row in right_table.findAll("tr"):

    cells = row.findAll('td')
    states=row.findAll('th') #To store second column data
    if len(cells)==6: #Only extract table body not heading
        A.append(cells[0].find(text=True))
        B.append(states[0].find(text=True))
        C.append(cells[1].find(text=True))
        D.append(cells[2].find(text=True))
        E.append(cells[3].find(text=True))
        F.append(cells[4].find(text=True))
        G.append(cells[5].find(text=True))

#import pandas to convert list to data frame

import pandas as pd
df=pd.DataFrame(A,columns=['Number'])
df['State/UT']=B
df['Admin_Capital']=C
df['Legislative_Capital']=D
df['Judiciary_Capital']=E
df['Year_Capital']=F
df['Former_Capital']=G
df

Similarly, you can perform various other types of web scraping using “Beautiful Soup“. This will reduce your manual efforts to collect data from web pages. You can also look at the other attributes like .parent, .contents, .descendants and .next_sibling, .prev_sibling and various attributes to navigate using tag name. These will help you to scrap the web pages effectively.-

But, why can’t I just use Regular Expressions?

Now, if you know regular expressions, you might be thinking that you can write code using regular expression which can do the same thing for you. I definitely had this question. In my experience with Beautiful Soup and Regular expressions to do same thing I found out:

Code written in Beautiful Soup is usually more robust than the one written using regular expressions. Codes written with regular expressions need to be altered with any changes in pages. Even Beautiful Soup needs that in some cases, it is just that Beautiful Soup is relatively better.

Regular expressions are much faster than Beautiful Soup, usually by a factor of 100 in giving the same outcome.

So, it boils down to speed vs. robustness of the code and there is no universal winner here. If the information you are looking for can be extracted with simple regex statements, you should go ahead and use them. For almost any complex work, I usually recommend BeautifulSoup more than regex.

End Note

In this article, we looked at web scraping methods using “Beautiful Soup” and “urllib2” in Python. We also looked at the basics of HTML and perform the web scraping step by step while solving a challenge. I’d recommend you to practice this and use it for collecting data from web pages.


 Source : http://www.analyticsvidhya.com/blog/2015/10/beginner-guide-web-scraping-beautiful-soup-python/