Scraping Data: Site-specific Extractors vs. Generic Extractors

Scraping is becoming a rather mundane job with every other organization getting its feet wet with it for their own data gathering needs. There have been enough number of crawlers built – some open-sourced and others internal to organizations for in-house utilities. Although crawling might seem like a simple technique at the onset, doing this at a large-scale is the real deal. You need to have a distributed stack set up to take care of handling huge volumes of data, to provide data in a low-latency model and also to deal with fail-overs. This still is achievable after crossing the initial tech barrier and via continuous optimizations. (P.S. Not under-estimating this part because it still needs a team of Engineers monitoring the stats and scratching their heads at times).

Social Media Scraping

Focused crawls on a predefined list of sites

However, you bump into a completely new land if your goal is to generate clean and usable data sets from these crawls i.e. “extract” data in a format that your DB can process and aid in generating insights. There are 2 ways of tackling this:

a. site-specific extractors which give desired results

b. generic extractors that result in few surprises

Assuming you still do focused crawls on a predefined list of sites, let’s go over specific scenarios when you have to pick between the two-

1. Mass-scale crawls; high-level meta data - Use generic extractors when you have a large-scale crawling requirement on a continuous basis. Large-scale would mean having to crawl sites in the range of hundreds of thousands. Since the web is a jungle and no two sites share the same template, it would be impossible to write an extractor for each. However, you have to settle in with just the document-level information from such crawls like the URL, meta keywords, blog or news titles, author, date and article content which is still enough information to be happy with if your requirement is analyzing sentiment of the data.

cb1c0_one-size

A generic extractor case

Generic extractors don’t yield accurate results and often mess up the datasets deeming it unusable. Reason being

programatically distinguishing relevant data from irrelevant datasets is a challenge. For example, how would the extractor know to skip pages that have a list of blogs and only extract the ones with the complete article. Or delineating article content from the title on a blog page is not easy either.

To summarize, below is what to expect of a generic extractor.

Pros-

minimal manual intervention

low on effort and time

can work on any scale

Cons-

Data quality compromised

inaccurate and incomplete datasets

lesser details suited only for high-level analyses

Suited for gathering- blogs, forums, news

Uses- Sentiment Analysis, Brand Monitoring, Competitor Analysis, Social Media Monitoring.

2. Low/Mid scale crawls; detailed datasets - If precise extraction is the mandate, there’s no going away from site-specific extractors. But realistically this is do-able only if your scope of work is limited i.e. few hundred sites or less. Using site-specific extractors, you could extract as many number of fields from any nook or corner of the web pages. Most of the times, most pages on a website share similar templates. If not, they can still be accommodated for using site-specific extractors.

cutlery

Designing extractor for each website

Pros-

High data quality

Better data coverage on the site

Cons-

High on effort and time

Site structures keep changing from time to time and maintaining these requires a lot of monitoring and manual intervention

Only for limited scale

Suited for gathering - any data from any domain on any site be it product specifications and price details, reviews, blogs, forums, directories, ticket inventories, etc.

Uses- Data Analytics for E-commerce, Business Intelligence, Market Research, Sentiment Analysis

Conclusion

Quite obviously you need both such extractors handy to take care of various use cases. The only way generic extractors can work for detailed datasets is if everyone employs standard data formats on the web (Read our post on standard data formats here). However, given the internet penetration to the masses and the variety of things folks like to do on the web, this is being overly futuristic.

So while site-specific extractors are going to be around for quite some time, the challenge now is to tweak the generic ones to work better. At PromptCloud, we have added ML components to make them smarter and they have been working well for us so far.

What have your challenges been? Do drop in your comments.

Source: https://www.promptcloud.com/blog/scraping-data-site-specific-extractors-vs-generic-extractors/

'Scrapers' Dig Deep for Data on Web

At 1 a.m. on May 7, the website PatientsLikeMe.com noticed suspicious activity on its "Mood" discussion board. There, people exchange highly personal stories about their emotional disorders, ranging from bipolar disease to a desire to cut themselves.

It was a break-in. A new member of the site, using sophisticated software, was "scraping," or copying, every single message off PatientsLikeMe's private online forums.

Enlarge Image

Bilal Ahmed wrote about his health on a site that was scraped. Andrew Quilty for The Wall Street Journal.

PatientsLikeMe managed to block and identify the intruder: Nielsen Co., the privately held New York media-research firm. Nielsen monitors online "buzz" for clients, including major drug makers, which buy data gleaned from the Web to get insight from consumers about their products, Nielsen says.

"I felt totally violated," says Bilal Ahmed, a 33-year-old resident of Sydney, Australia, who used PatientsLikeMe to connect with other people suffering from depression. He used a pseudonym on the message boards, but his PatientsLikeMe profile linked to his blog, which contains his real name.

After PatientsLikeMe told users about the break-in, Mr. Ahmed deleted all his posts, plus a list of drugs he uses. "It was very disturbing to know that your information is being sold," he says. Nielsen says it no longer scrapes sites requiring an individual account for access, unless it has permission.

Related Reading

    Digits: Escaping the 'Scrapers'
    Complete Coverage: What They Know

Journal Community

The market for personal data about Internet users is booming, and in the vanguard is the practice of "scraping." Firms offer to harvest online conversations and collect personal details from social-networking sites, résumé sites and online forums where people might discuss their lives.

The emerging business of web scraping provides some of the raw material for a rapidly expanding data economy. Marketers spent $7.8 billion on online and offline data in 2009, according to the New York management consulting firm Winterberry Group LLC. Spending on data from online sources is set to more than double, to $840 million in 2012 from $410 million in 2009.

The Wall Street Journal's examination of scraping—a trade that involves personal information as well as many other types of data—is part of the newspaper's investigation into the business of tracking people's activities online and selling details about their behavior and personal interests.

Some companies collect personal information for detailed background reports on individuals, such as email addresses, cell numbers, photographs and posts on social-network sites.

Others offer what are known as listening services, which monitor in real time hundreds or thousands of news sources, blogs and websites to see what people are saying about specific products or topics.

One such service is offered by Dow Jones & Co., publisher of the Journal. Dow Jones collects data from the Web—which may include personal information contained in news articles and blog postings—that help corporate clients monitor how they are portrayed. It says it doesn't gather information from password-protected parts of sites.

It's rarely a coincidence when you see Web ads for products that match your interests. WSJ's Christina Tsuei explains how advertisers use cookies to track your online habits.

The competition for data is fierce. PatientsLikeMe also sells data about its users. PatientsLikeMe says the data it sells is anonymized, no names attached.

Nielsen spokesman Matt Anchin says the company's reports to its clients include publicly available information gleaned from the Internet, "so if someone decides to share personally identifiable information, it could be included."

Internet users often have little recourse if personally identifiable data is scraped: There is no national law requiring data companies to let people remove or change information about themselves, though some firms let users remove their profiles under certain circumstances.

California has a special protection for public officials, including politicians, sheriffs and district attorneys. It makes it easier for them to remove their home address and phone numbers from these databases, by filling out a special form stating they fear for their safety.

Data brokers long have scoured public records, such as real-estate transactions and courthouse documents, for information on individuals. Now, some are adding online information to people's profiles.

Many scrapers and data brokers argue that if information is available online, it is fair game, no matter how personal.

"Social networks are becoming the new public records," says Jim Adler, chief privacy officer of Intelius Inc., a leading paid people-search website. It offers services that include criminal background checks and "Date Check," which promises details about a prospective date for $14.95.

"This data is out there," Mr. Adler says. "If we don't bring it to the consumer's attention, someone else will."

Scraping for Your Real Name

PeekYou.com has applied for a patent for a way to, among other things, match people's real names to pseudonyms they use on blogs, Twitter and online forums.

Read PeekYou.com's patent application.

Enlarge Image

New York-based PeekYou LLC has applied for a patent for a method that, among other things, matches people's real names to the pseudonyms they use on blogs, Twitter and other social networks. PeekYou's people-search website offers records of about 250 million people, primarily in the U.S. and Canada.

PeekYou says it also is starting to work with listening services to help them learn more about the people whose conversations they are monitoring. It says it hands over only demographic information, not names or addresses.

Employers, too, are trying to figure out how to use such data to screen job candidates. It's tricky: Employers legally can't discriminate based on gender, race and other factors they may glean from social-media profiles.

One company that screens job applicants for employers, InfoCheckUSA LLC in Florida, began offering limited social-networking data—some of it scraped—to employers about a year ago. "It's slowly starting to grow," says Chris Dugger, national account manager. He says he's particularly interested in things like whether people are "talking about how they just ripped off their last employer."

Scrapers operate in a legal gray area. Internationally, anti-scraping laws vary. In the U.S., court rulings have been contradictory. "Scraping is ubiquitous, but questionable," says Eric Goldman, a law professor at Santa Clara University. "Everyone does it, but it's not totally clear that anyone is allowed to do it without permission."

Scrapers and listening companies say what they're doing is no different from what any person does when gathering information online—they just do it on a much larger scale.

"We take an incomprehensible amount of information and make it intelligent," says Chase McMichael, chief executive of InfiniGraph, a Palo Alto, Calif., "listening service" that helps companies understand the likes and dislikes of online customers.

Scraping services range from dirt cheap to custom-built. Some outfits, such as 80Legs.com in Texas, will scrape a million Web pages for $101. One Utah company, screen-scraper.com, offers do-it-yourself scraping software for free. The top listening services can charge hundreds of thousands of dollars to monitor and analyze Web discussions.

Some scrapers-for-hire don't ask clients many questions.

"If we don't think they're going to use it for illegal purposes—they often don't tell us what they're going to use it for—generally, we'll err on the side of doing it," says Todd Wilson, owner of screen-scraper.com, a 10-person firm in Provo, Utah, that operates out of a two-room office. It is one of at least three firms in a scenic area known locally as "Happy Valley" that specialize in scraping.

Enlarge Image

Some of the computer code behind screen-scraper.com's software. Chris Detrick for The Wall Street Journal

Screen-scraper charges between $1,500 and $10,000 for most jobs. The company says it's often hired to conduct "business intelligence," working for companies who want to scrape competitors' websites.

One recent assignment: A major insurance company wanted to scrape the names of agents working for competitors. Why? "We don't know," says Scott Wilson, the owner's brother and vice president of sales. Another job: attempting to scrape Facebook for a multi-level marketing company that wanted email addresses of users who "like" the firm's page—as well as their friends—so they all could be pitched products.

Scraping often is a cat-and-mouse game between websites, which try to protect their data, and the scrapers, who try to outfox their defenses. Scraping itself isn't difficult: Nearly any talented computer programmer can do it. But penetrating a site's defenses can be tough.

One defense familiar to most Internet users involves "captchas," the squiggly letters that many websites require people to type to prove they're human and not a scraping robot. Scrapers sometimes fight back with software that deciphers captchas.

More From the Series

    Web's New Goldmine: Your Secrets

    Personal Details Exposed Via Biggest Websites

    Microsoft Quashed Bid to Boost Web Privacy

    On Web's Cutting Edge, Anonymity in Name Only

    Stalking by Cellphone

    Google Agonizes Over Privacy

    The Tracking Ecosystem

    On the Web, Children Face Intensive Tracking

Some professional scrapers stage blitzkrieg raids, mounting around a dozen simultaneous attacks on a website to grab as much data as quickly as possible without being detected or crashing the site they're targeting.

Raids like these are on the rise. "Customers for whom we were regularly blocking about 1,000 to 2,000 scrapes a month are now seeing three times or in some cases 10 times as much scraping," says Marino Zini, managing director of Sentor Anti Scraping System. The company's Stockholm team blocks scrapers on behalf of website clients.

At Monster.com, the jobs website that stores résumés for tens of millions of individuals, fighting scrapers is a full-time job, "every minute of every day of every week," says Patrick Manzo, global chief privacy officer of Monster Worldwide Inc. Facebook, with its trove of personal data on some 500 million users, says it takes legal and technical steps to deter scraping.

At PatientsLikeMe, there are forums where people discuss experiences with AIDS, supranuclear palsy, depression, organ transplants, post-traumatic stress disorder and self-mutilation. These are supposed to be viewable only by members who have agreed not to scrape, and not by intruders such as Nielsen.

"It was a bad legacy practice that we don't do anymore," says Dave Hudson, who in June took over as chief executive of the Nielsen unit that scraped PatientsLikeMe in May. "It's something that we decided is not acceptable, and we stopped."

Mr. Hudson wouldn't say how often the practice occurred, and wouldn't identify its client.

The Nielsen unit that did the scraping is now part of a joint venture with McKinsey & Co. called NM Incite. It traces its roots to a Cincinnati company called Intelliseek that was founded in 1997. One of its most successful early businesses was scraping message boards to find mentions of brand names for corporate clients.

In 2001, the venture-capital arm of the Central Intelligence Agency, In-Q-Tel Inc., was among a group of investors that put $8 million into the business.

Intelliseek struggled to set boundaries in the new business of monitoring individual conversations online, says Sundar Kadayam, Intelliseek's co-founder. The firm decided it wouldn't be ethical to use automated software to log into private message boards to scrape them.

But, he says, Intelliseek occasionally would ask employees to do that kind of scraping if clients requested it. "The human being can just sign in as who they are," he says. "They don't have to be deceitful."

In 2006, Nielsen bought Intelliseek, which had revenue of more than $10 million and had just become profitable, Mr. Kadayam says. He left one year after the acquisition.

At the time, Nielsen, which provides television ratings and other media services, was looking to diversify into digital businesses. Nielsen combined Intelliseek with a New York startup it had bought called BuzzMetrics.

The new unit, Nielsen BuzzMetrics, quickly became a leader in the field of social-media monitoring. It collects data from 130 million blogs, 8,000 message boards, Twitter and social networks. It sells services such as "ThreatTracker," which alerts a company if its brand is being discussed in a negative light. Clients include more than a dozen of the biggest pharmaceutical companies, according to the company's marketing material.

Like many websites, PatientsLikeMe has software that detects unusual activity. On May 7, that software sounded an alarm about the "Mood" forum.

David Williams, the chief marketing officer, quickly determined that the "member" who had triggered the alert actually was an automated program scraping the forum. He shut down the account.

The next morning, the holder of that account e-mailed customer support to ask why the login and password weren't working. By the afternoon, PatientsLikeMe had located three other suspect accounts and shut them down. The site's investigators traced all of the accounts to Nielsen BuzzMetrics.

On May 18, PatientsLikeMe sent a cease-and-desist letter to Nielsen. Ten days later, Nielsen sent a letter agreeing to stop scraping. Nielsen says it was unable to remove the scraped data from its database, but a company spokesman later said Nielsen had found a way to quarantine the PatientsLikeMe data to prevent it from being included in its reports for clients.

PatientsLikeMe's president, Ben Heywood, disclosed the break-in to the site's 70,000 members in a blog post. He also reminded users that PatientsLikeMe also sells its data in an anonymous form, without attaching user's names to it. That sparked a lively debate on the site about the propriety of selling sensitive information. The company says most of the 350 responses to the blog post were supportive. But it says a total of 218 members quit.

In total, PatientsLikeMe estimates that the scraper obtained about 5% of the messages in the site's forums, primarily in "Mood" and "Multiple Sclerosis."

Source: http://online.wsj.com/articles/SB10001424052748703358504575544381288117888

My Experience in Choosing a Web Scraping Service

Recently I decided to outsource a web scraping project to another company. I typed “web scraping service” in Google, chose six services from the first two search result pages and sent the project specifications to all of them to get quotes. Eventually I decided to go another way and did not order the services, but my experience may be useful for others who want to entrust web scraping jobs to third party services.

If you are interested in price comparisons only and not ready to read the whole story just scroll down.

A list of web scraping services I sent my project to:

    www.datahen.com - Canadian web scraping service with nice web design
    webdata-scraping.com - Indian service by Keval Kothari
    www.iwebscraping.com - India based web scraping company (same as www.3idatascraping.com)
    scrapinghub.com - A scraping service founded by creators of Scrapy
    web-scraper.com - Yet another web scraping service
    grepsr.com - A scraping service that we already reviewed two years ago

Sending the request

All the services except scrapinghub.com have quite simple forms for the description of the project requirements. Basically, you just need to give your contact details and a project description in any form. Some of them are pretty (like datahen.com), some of them are more ascetic (like web-scraper.com), but all of them allow you to send your requirements to developers.

Scrapinghub.com has a quite long form, but most of the fields are optional and all the questions are quite natural. If you really know what you need, then it won’t be hard to answer all of them; moreover they rather help you to describe your need in detail.

Note, that in the context of the project I didn’t make a request for a scraper itself. I asked to receive data on a weekly basis only.

Getting responses

Since I sent my request on Sunday it would have been ok not to receive responses the same day, but I got the first response in 3 hrs! It was from web-scraper.com and stated that this project will cost me $250 monthly. Simple and clear. Thank you, Thang!

Right after that, I received the second response. This time it was Keval from webdata-scraping.com. He had some questions regarding the project. Then after two days he wrote me that it would be hard to scrape some of my data with the software he uses, and that he will try to use a custom scraper. After that he disappeared… ((

Then on Monday I received Cost & ETAT details from datahen.com. It looked quite professional and contained not only price, but also time estimation. They were ready to create such a scraper in 3-4 days for $249 and then maintain it for just $65/month.

On the same day I received a quote from iwebscraping.com. It was $60 per week. Everything is fine, but I’d like to mention that it wasn’t the last letter from them. After I replied to them (right after receiving the quote), I received a reminder letter from them every other day for about a week. So be ready for aggressive marketing if you ask them for a quote )).

Finally in two days after requesting a quote I got a response from scrapinghub.com. Paul Tremberth wrote me that they were ready to build a scraper for $1200 and then maintain it for $300/month.

It is interesting that I have never received an answer from grepsr.com! Two years ago it was the first web scraping service we faced on the web, but now they simply ignored my request! Or perhaps they didn’t receive it somehow? Anyway I had no time for investigation.

So what?

Let us put everything together. Out of six web scraping  services I received four quotes with the following prices:

Service     Setup fee     Monthly fee

web-scraper.com     -     $250
datahen.com     $249     $65
iwebscraping.com     -     $240
scrapinghub.com     $1200     $300

From this table you can see that  scrapinghub.com appears to be the most expensive service among those compared.

EDIT: These $300/month gives you as much support and development needed to fix a 5M multi-site web crawler, for example. If you need a cheaper solution you can use their Autoscraping tool, which is free, and would have costed around $2/month to crawl at my requested rates.

The average cost of monthly scraping is about $250, but from a long term perspective datahen.com may save you money due to their low monthly fee.

That’s it! If I had enough money available it would be interesting to compare all these services in operation and provide you a more complete report, but this is all I have for now.

If you have anything to share about your experience in using similar services, please contribute to this post by commenting on it below. Cheers!

Source: http://scraping.pro/choosing-web-scraping-service/

Why People Hesitate To Try Data Mining

What is hindering a number of people from venturing into the promising world of data mining? Despite so much encouragement, promotions, testimonials, and evidences of the benefits of online data collection, still only a handful take the challenge and really gain the pay offs it has to offer.

It may sound unthinkable that such an opportunity for success has been neglected by many. It may also sound absurd why many well-meaning individuals are hindered from enjoying the benefits of the blessings of the 21st century.

The Causes

After considerable observation and analysis of the human psyche, one can understand the underlying reasons behind the hesitance to try the profitable data mining service. The most common reasons why people are afraid to try new technology or why they remain passive and uninvolved are: fear; lack of knowledge; and pride.

Fear. The most paralyzing of human emotions is fear. It can, to some extent, cause a person to be insane, unprofitable, sick, and lost. Although fear is a normal reaction to certain stimuli and a natural feeling experienced by humans, it must always be monitored and controlled.  Usually, people share common fears, such as: fear of change; fear of anything new; and fear of the unknown.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/people-hesitate-try-data-mining/

Why Web Scraping is Indispensable

The 21st century has opened the gates to hidden treasures and unlimited access to information globally without the constraints of time and space, through Internet technology. Along with this development comes the necessity for each business or company to get as much information as possible in order in order to thrive in the ever increasing demand for new innovations, comparisons, and trends.

Web scraping has consequently become an indispensable option to achieve all the needed data as quickly and efficiently as possible. In this view, data mining then appears to be the best and the only way to answer the present demand for updates, data, coping, foreknowledge, analysis, and evaluation. Indeed, information has inevitably become a valuable commodity and the most sought after product among online and offline entrepreneurs.

Need for Data

The increasing need for new data makes it possible for the experts to become increasingly creative in accessing information worldwide. The more knowledge one has, the better are his or her chances of growing and surviving. There seems to be no other time in the human existence where data has become so much a major source of revenue as the contemporary times.

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

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

Web data scraping (online news comments) with Scrapy (Python)

Since you seem like the try-first ask-question later type (that's a very good thing), I won't give you an answer, but a

(very detailed) guide on how to find the answer.

The thing is, unless you are a yahoo developer, you probably don't have access to the source code you're trying to

scrape. That is to say, you don't know exactly how the site is built and how your requests to it as a user are being

processed on the server-side. You can, however, investigate the client-side and try to emulate it. I like using Chrome

Developer Tools for this, but you can use others such as FF firebug.

So first off we need to figure out what's going on. So the way it works, is you click on the 'show comments' it loads

the first ten, then you need to keep clicking for the next ten comments each time. Notice, however, that all this

clicking isn't taking you to a different link, but lively fetches the comments, which is a very neat UI but for our

case requires a bit more work. I can tell two things right away:

    They're using javascript to load the comments (because I'm staying on the same page).
    They load them dynamically with AJAX calls each time you click (meaning instead of loading the comments with the

page and just showing them to you, with each click it does another request to the database).

Now let's right-click and inspect element on that button. It's actually just a simple span with text:

<span>View Comments (2077)</span>

By looking at that we still don't know how that's generated or what it does when clicked. Fine. Now, keeping the

devtools window open, let's click on it. This opened up the first ten. But in fact, a request was being made for us to

fetch them. A request that chrome devtools recorded. We look in the network tab of the devtools and see a lot of

confusing data. Wait, here's one that makes sense:

http://news.yahoo.com/_xhr/contentcomments/get_comments/?content_id=42f7f6e0-7bae-33d3-aa1d-

3dfc7fb5cdfc&_device=full&count=10&sortBy=highestRated&isNext=true&offset=20&pageNumber=2&_media.modules.content_commen

ts.switches._enable_view_others=1&_media.modules.content_comments.switches._enable_mutecommenter=1&enable_collapsed_com

ment=1

See? _xhr and then get_comments. That makes a lot of sense. Going to that link in the browser gave me a JSON object

(looks like a python dictionary) containing all the ten comments which that request fetched. Now that's the request you

need to emulate, because that's the one that gives you what you want. First let's translate this to some normal reqest

that a human can read:

go to this url: http://news.yahoo.com/_xhr/contentcomments/get_comments/
include these parameters: {'_device': 'full',
          '_media.modules.content_comments.switches._enable_mutecommenter': '1',
          '_media.modules.content_comments.switches._enable_view_others': '1',
          'content_id': '42f7f6e0-7bae-33d3-aa1d-3dfc7fb5cdfc',
          'count': '10',
          'enable_collapsed_comment': '1',
          'isNext': 'true',
          'offset': '20',
          'pageNumber': '2',
          'sortBy': 'highestRated'}

Now it's just a matter of trial-and-error. However, a few things to note here:

    Obviously the count is what decides how many comments you're getting. I tried changing it to 100 to see what

happens and got a bad request. And it was nice enough to tell me why - "Offset should be multiple of total rows". So

now we understand how to use offset

    The content_id is probably something that identifies the article you are reading. Meaning you need to fetch that

from the original page somehow. Try digging around a little, you'll find it.

    Also, you obviously don't want to fetch 10 comments at a time, so it's probably a good idea to find a way to fetch

the number of total comments somehow (either find out how the page gets it, or just fetch it from within the article

itself)

    Using the devtools you have access to all client-side scripts. So by digging you can find that that link to

/get_comments/ is kept within a javascript object named YUI. You can then try to understand how it is making the

request, and try to emulate that (though you can probably figure it out yourself)

    You might need to overcome some security measures. For example, you might need a session-key from the original

article before you can access the comments. This is used to prevent direct access to some parts of the sites. I won't

trouble you with the details, because it doesn't seem like a problem in this case, but you do need to be aware of it in

case it shows up.

    Finally, you'll have to parse the JSON object (python has excellent built-in tools for that) and then parse the

html comments you are getting (for which you might want to check out BeautifulSoup).

As you can see, this will require some work, but despite all I've written, it's not an extremely complicated task

either.

So don't panic.

It's just a matter of digging and digging until you find gold (also, having some basic WEB knowledge doesn't hurt).

Then, if you face a roadblock and really can't go any further, come back here to SO, and ask again. Someone will help

you.


Source: http://stackoverflow.com/questions/20218855/web-data-scraping-online-news-comments-with-scrapy-python