Monday, September 28, 2020

How to Improve Business Outcome Using Web Scraping Technology?

Irrespective of the type of business you operate, data is very important for a successful business outcome.

If you run an ecommerce business, you would need data from other ecommerce sites for ship dropping, market trend analysis, price comparison, analysis of sales offer and strategy, etc.

If your business produces products and services, you would need data for consumer behavior analysis, sentiment analysis, etc.

More importantly, every business sales start with lead generation, which is one of the most important parts of any business sales process, that is why every business needs to always update their pool of leads to find ideal customers.

However, quality sales lead generation is not always easy as it involves several activities. But recently, the lead generation process has been optimized, thanks to sales lead generation scraping tools. Business owners can now use sales lead scrapers to extract sales leads from the Internet for their business success without having to write any code. Such sales lead generation tool are made up of based on web scraping technology. Web scraping service is automated and fastest coding method for data grabbing from various sites.

Sales leads are available all over the internet, from websites to social media platforms and directory sites. These leads can be extracted and used by your business for more successful sales efforts. More so, sales lead scrapers can help
business owners to dictate or know where their sales leads are coming from. All you have to do is type the market type, country, region, and city you want to get sales leads from and the sales lead scraping tool will do the rest of the task.

There are several web scraping companies that provide sales lead scraping services , which makes getting the best one an arduous task. However, one of the trusted and tested sales lead scraping services that can professionally extract any type of data for your business success is the Infovium web scraping services.

source: https://medium.com/@getalladvice/how-to-improve-business-outcome-using-web-scraping-technology-2bf8d062c1f2

Sunday, May 24, 2020

Guide for Smart Investing in Stock Markets

How LinkedIn Company Data Helps in Tracking Employee Count


LinkedIn contains over 500 Million profiles of professional members, who are entrepreneurs, currently employed in a company, seeking better job placement, or seeking new employment. How to collect these all data – option is scraping Linkedin.
If you’re member of the sales and marketing team, you would understand the importance of tracking employee count on LinkedIn. Employee count is used to determine a company’s size. It’s a crucial data point for most common sales and marketing activities, such as segmentation, lead routing, outbound outreach, territory planning, etc.
The size of a company’s employee will determine certain tools needed by the company. For instance, a company with 100 employees may not need an applicant tracking system while a company with 300 employees needs one.
More so, some certain B2B companies may be prospecting to companies of a certain size too. So, such companies will find appropriate companies with the necessary number of employee count to make a deal.
Now that we’ve seen why company size is important, let’s see how LinkedIn data can help in tracking employee count.
Like stated above, LinkedIn also contains a large database of professionals who are currently employed in a company. Therefore, profiles extraction from LinkedIn is helpful to find out where these professionals are employed. This will, in turn, help to determine the number of employees in a particular company.
Scraping LinkedIn is, therefore, required to collect necessary data from these LinkedIn profiles. Scraping LinkedIn quickly and efficiently requires the service of a professional Linkedin data extractor. However which are helpful headers can be collected using Linkedin data scraper?
LinkedIn contains professional profiles that showcase qualifications, skills, experience, expertise, and most especially your current employer, which can be used by the sales and marketing team to track employee count of companies.

Scraping LinkedIn Data can be Used for Other Purposes:

  • Keeping job databases updated by HR and staffing agents.
  • Fuelling smaller job aggregator boards with fresh job listing data.
  • Analyzing the labor market, salary trends, and job trends within a country.
  • Tracking competitors’ vacant job openings, salary/compensations, and other benefit plans to be able to stand up to the competition by making better offers, etc.
Final Note
LinkedIn data has proven to be very useful and effective for tracking employee count, which is needed in making several business decisions. And the only way to go about extracting this data is by using a LinkedIn data extractor.

Thursday, May 14, 2020

Keep An Eye On Your Competition Using Amazon Scraping


Amazon is a great online marketplace, where any wise retailer can make a huge profit. Anybody can utilize Amazon for their product sales – individual sellers, small retailers, large retailers, etc. This is because Amazon records the visit of a very large number of buyers and prospective buyers daily. This is a good opportunity for anyone to consider Amazon for ecommerce business. Let know how Amazon scraping is beneficial for you to become best seller.
As Amazon witnesses a large number of buyers daily and also have a large number of sellers, who are constantly competing to service these buyers. If you are a seller on Amazon, you are currently facing lots of competitions. So you need to find ways to either hedge off your competitors or outperform them.
One of the ways to achieve this is to keep an eye on your competition using Amazon web scraping. Keeping an eye on your competitors will help you to increase your visibility and sales on Amazon.
Some of the important things you need to consider winning on your competitions on Amazon to boost your product sales:

Your Competitor’s Product Price

This is the first thing you have to keep your eyes on as product price. It  is the biggest differentiator that sets sellers apart on Amazon. If your product is pricey, your “supposed” prospects will turn to your competitors. So, you have to always keep an eye on your competitor’s price.
This will help you to draw an insight that will make you price your product competitively. Your price shouldn’t be too high or too low. Therefore an easy way to gain an insight into your competitor’s pricing is to scrape prices from your competitors’ products.

Your Competitor’s Product Title and Description

Another thing to focus on is the product title and description. Your product title will determine if your product will show up to your prospect or not if a related product is searched for. Your product title will also determine if your prospect will visit your product page if your product eventually appears to them.
Remember, your product title must be catchy enough to attract your buyer’s attention and entice them to click to the page, where your product description does the rest of the work to compel your prospect to make a purchase.
To gain an insight into how to do this better, you can use an Amazon data scraper to scrape product titles and description from your competitors to develop your own product title and description. See more on How to boost sell on Amazon?
Conclusion
Scraping Amazon data will enable you to take advantage of the tips listed above to always keep an eye on your competition using Amazon scraping tools. If you need a product scraping from Amazon, Infovium is simply one of the best in the market. For sample file visit Portfolio.

Sunday, May 3, 2020

Why Xing Scraper Is Demanding In European Business Network?

Xing Scraper

Have you heard about Xing scraper? Just like LinkedIn, Xing is an online social networking site for business professionals. The main objective of Xing is to assist users to find jobs or contact other business experts. Xing is a European networking platform and contains the contact information of millions of business professionals worldwide. This makes the platform suitable for European business networking.
Xing users can access details of several business professionals as well as collect required data of any business professional registered on Xing. These details and data can be used to build a strong European business network. However, this would require extracting a large amount of data from Xing. Except you require a few amounts of business contacts on Xing (which may not be sufficient to build a large business network), using Xing scraper would help you to extract and collate thousands of details of business professionals from Xing within a very short time.
Xing scraper is an ideal tool to extract necessary data of business professionals from Xing based on your requirements. It doesn’t only save time and make extraction process easy and efficient but also streamlines whole process for better extraction.
Unlike with manual scraping, extracting data with Xing lead extractor (see sample file) gives access to millions of professionals, service providers, and potential clients in a short time.

Xing Scraping Can Extract the Following Data Fields from Xing Website:


  1. Job Title, job description, job type, job URL, job location, job rating by employees, job reviews, keywords, company, industry, date posted.
  2. Company name, company description, company address, company location and address, company telephone/contact number, company website and email, company URLs.
  3. People’s names, people’s details, people’s experience, people’s URLs, people’s designation, people’s address and contact info, interests, profile link, occupation, qualifications, etc.
Users can extract data from Xing using Xing scraping by using specific search keywords to locate target business professionals. That means Xing scraper uses advanced tools that search and collate data by specific keywords, experience levels, industry, company, location.  To avoid duplication of data, scraper saves the history of the viewed and saved business profiles.
A Xing scraper is a must-have tool if you need to build an excellent European business network. However, there are several web scraping software providers out there and choosing the right one may be a herculean task. Nevertheless, a trusted and tested Xing scraper provider is Infovium. We offer the perfect Xing data scraping service to crawl and extract data on profiles on Xing.
Our social media data scraping is not only secured but also scalable and enables users to execute their projects within a short time. We are expert in scraping Facebook, Linkedin, Twitter, Instagram like social sites. Hence, if you are in need of a Xing scraper, Infovium is simply one of the best in the market.

Wednesday, March 18, 2020

How Scraping Technology Makes Sentiment Analysis Easy?

Sentiment Analysis Data Scraping

Sentiment analysis is a process of identifying, collating, categorizing, and analyzing opinions expressed in pieces of text – posts, comments, tweets, replies, etc. – about a particular product or service to determine the writers’ attitude about the product or service whether it is positive, neutral, or negative. Easy and fast analysis is only possible with sentiment analysis data scraping services.
As a savvy business owner, sentiment analysis is a very good tool that you can use to draw business insight. The best place to pull data for sentiment analysis is social media. Yes! Social media is a very good platform to obtain data to run a sentiment analysis on your product. In addition, Facebook is currently the largest and most popular social media platform with the largest number of audiences. Hence, Facebook data is the best choice for sentiment analysis. To collect data from Facebook sentiment analyzer highly depend on Facebook scraper. However, Twitter, Linkedin, Reddit, Forum, etc. are also other options from where data extraction is possible for analysis.
There are a lot of data on social media that business owners who show concern about their online reputation can use for sentiment analysis. Hence, you would need a means of extracting this data for sentiment analysis.

Use of web scraping sentiment analysis

  • Watch on your rivals
  • Collecting your customer’s reviews
  • Investigation for business knowledge
  • Feedback on your product
However, extracting data for sentiment analysis shouldn’t be a difficult task. Most social media channels and forums have APIs that users can use to quickly extract data. But these APIs cannot extract relevant data for sentiment analysis. Data Extraction for sentiment analysis requires keywords or search term that relates to the product or service to be analyzed.
On the other hand, manual data extraction is not an option because of its time-consuming nature. So the only way to get clean and well-structured data from social media and forums without hassle is by using sentiment analysis data scraping services.
The data generated from sentiment analysis data scraper can help you to quickly find out how people are relating to your products on social media.
Data scraper for sentiment analysis will relieve you of all the complicated processes involved in scraping social media and forums. The only time you need to do is to provide the name or keyword of the product or service to search for, the frequency of extraction, and the format of output – HTML, TXT, CSV, XML, JSON, etc.
If you want to know what people are saying about your brand, products, or services on social media channels, you need the service of a professional sentiment analysis data scraping services to extract social media data for sentiment analysis. Contact us today to discuss about using our sentiment analysis data scraping service.

Thursday, March 5, 2020

What Are The Benefits Of Scraping Bigbasket Data?

Bigbasket scraper


Are You Aware About Bigbasket Data Scraping ?

Bigbasket data scraping is way to collect products information listing on it by automatically. Fastest way to extract bulk amount of data from it is Bigbasket scraper tool and also easy to operate.
Bigbasket is one of the best online grocers in India. It is a large ecommerce store that supplies a wide range of daily needs, which include fresh fruits and vegetables, daily products, best-quality food grains and pulses, and lots of other branded items.
Being a large ecommerce site, Bigbasket contains a large amount of product data that can be scraped and used by individual shoppers, resellers, and ship droppers. But what are the benefits that Bigbasket data scraping holds for these users?
Foremost, it is very useful for shop dropping websites, smaller online retail stores, or resellers. It enables them to extract product data from Bigbasket in the automated way.
Ship droppers and resellers can use this product data to fill in their ecommerce store to attract customers. Other ecommerce stores can use this data for price analysis, product comparison, marking best-selling products. Individuals can use this data for checking customer ratings and reviews before making a purchase.

Product Data Can Be Collected By Bigbasket Scraper:

  • Product name,
  • Description,
  • Brand name,
  • Weight,
  • URL of product,
  • Price (special price and MRP price)
  • Food type
  • Nutrition details
  • Ingredients
  • Category
  • Sub-category
  • Customer ratings
  • Reviews, etc.
As stated above scraping Bigbasket has several benefits. Hence, if you’re involved in ecommerce business or you’re a frequent shopper, scraping Bigbasket data is important to you and Bigbasket scraper software is a must for affordable and quick access to Bigbasket data.
While there are several types of Bigbasket scraper, outsourcing to a Bigbasket scraping service provider. You can check our Bigbasket product scraping sample file that we have scraped previously. This method ensures that you get product data from Bigbasket in a clean and well-arranged manner. You would also get the data in the right format that is easily accessible.
We pride ourselves as one of the best web scraping services providers. We subject all extracted data to deep scrutiny to eliminate duplicate data, wrong data, and invalid URLs to ensure we deliver only the best quality of product data to our customers. Our team have huge experience in scraping Ecommerce websites like Amazon, Aliexpress, Walmart, Ebay and more. Even we are working Daily basis data extraction from Amazon and Aliexpress.
Irrespective of the number of product details you want to scrape on Bigbasket, our automated Bigbasket scraper service will deliver the result within a short time. Fulfilling our customer’s needs is our priority that is why we always deliver our data scraping services exactly as we promise. For more information on how we can help your Ecommerce business with our service, contact us today.

Saturday, February 22, 2020

Why LinkedIn Scraping Is Become Popular Now Days?

Linkedin Scraping
Now days LinkedIn scraping is demand of huge mass. Question arise is what are the benefits and uses of LinkedIn data. This article includes about LinkedIn scraping and its application and uses. It includes both LinkedIn profile and company pages data scraping.

To Aggregate Jobs Listing Data

Recently, job listing data has become one of the most sought-after data on the Internet. Almost every organization is looking for a professional who can handle their jobs and everybody needs a job or at least a better job placement.
The percentage of those who currently have no job and seeking for jobs online is high and keeps increasing as more graduates get into the labor market. These job seekers turn to LinkedIn to create a profile to showcase their qualifications, skills, experience, expertise, etc. Business organizations can also turn to LinkedIn to seek qualified personnel to handle their requirements. 

To Gather Company Details for Venture Capital & Private Equity Firms

Venture capital and private equity firm are investing in leading Information, Software, Industrial and Healthcare Technology companies. They need to collect company data like no of employees, year of establishment, category, specialize etc. for analysis purpose and based on that they plan about their future investment.

LinkedIn Data Can Be Used for Many Various Purposes:

  • Keeping job databases updated by HR and staffing agents.
  • Fuelling smaller job aggregator boards with fresh job listing data.
  • Analyzing the labor market, salary trends, and job trends within a country.
  • Tracking competitors’ vacant job openings, salary/compensations, and other benefit plans to be able to stand up to the competition by making better offers, etc.
LinkedIn contains over 500 Million profiles of professional members, which companies can access to build a professional network. Scraping LinkedIn is therefore necessary to collect data from these profiles and company pages.
A quick and efficient LinkedIn scraping requires the service of a professional data scraping company that will crawl the LinkedIn website and extract the entire profile data on it. It is not an easy task to extract data from LinkedIn. It requires expertise as LinkedIn has a special anti-scraping technique that prevents excessive page crawling and scraping. Also day by day LinkedIn changes its security. So web scraping services that have experience in data extraction from such sites like LinkedIn can fulfill your requirement.
However, Infovium web scraping services reportedly have a reputation of being able to bypass this anti-scraping technique to give clients the best result. Hence, we can deliver highly customizable and well-structured results tailored to clients’ specific requirements and needs.
Contact us today to discuss your LinkedIn scraping needs. Further more check our sample file for LinkedIn data scraper.

Monday, February 10, 2020

How To Collect Amazon Data Quickly?

Amazon web scraping

Have you heard about the word Scrape Amazon seller prices and Scrape Amazon reviews? Yes, then why these two words are become very popular among Ecommerce businesses? How scraping useful for collecting prices and reviews from Amazon? This article describes all the answers for above questions.
Amazon is one of the popular and largest Ecommerce sites in the world. It consists of a host of products for sale. All these products have their product data associated with them. For instance, every product on Amazon has its name, URL, description, ID, specification, images, and pricing. All these constitute Amazon data, which can be found on the Amazon website.

Why Amazon Data Collection Is Popular?

Amazon data can be collected and used for several different purposes like personal and business purposes, especially marketing trend analysis, product price comparison, etc. Smart business owners, especially those in the e-commerce industry, can use this data for insights to develop competitive intelligence, edge against competitions, and gain more business advantages.
More so, Amazon data can help business owners in their decision-making process and to monitor their competitors. This makes collecting Amazon data very necessary and people in need of Amazon data seek the most reliable way of collecting data from Amazon.
Smart consumers can as well use Amazon data as a reference while pricing similar products on other Ecommerce stores. But how one can make this data collection process from Amazon quickly?

Various Ways Of Data Collection:

Most people settle to manually extract Amazon data, which is fine as long as only few Amazon data is needed. But for a large amount of data, manual extraction is nothing but a waste of time.
Some crawling tools like R software, Python, Script, Atom, can also be used for Amazon data extraction. But aside from the fact that these tools require level of coding that make them unsuitable for the layperson. The generated results are usually unstructured and may contain duplicates and errors, which make them unfit as a quick way of collecting Amazon data.
The only trusted way to extract Amazon data quickly is Amazon web scraping services.
Amazon data extraction services can easily and conveniently scrape fields like product title, product URL, product image, product price, product reviews, country of the seller, product shipping details, etc. and organize them in a well-structured format, ensuring no duplicate or error.
Mainly, Ecommerce businesses are demanding collection of customer reviews and seller prices. Scrape Amazon seller prices provides pricing details for price comparison that vary day to day. Same way, some owner may need only customer reviews for feedback of their new launching products. So in such case Amazon reviews scraper is useful for them to scrape Amazon reviews.
Hence, using Amazon data extraction services is the best way to quickly collect amazon data. If you’re looking for the best Amazon scraping service to help you collect Amazon data quickly and most effectively, contact us today for a free quote. Not only scraping Amazon data, we have years of experience in scraping Ecommerce websites like Walmart, Ebay, Aliexpress, Bigbasket and many more.
By the way, you can watch this video if interested in learning about Amazon data scraping using Python.

Tuesday, January 28, 2020

Best Way to Grab Aliexpress Products

Scraping Aliexpress

Are you looking for the best way for scraping Aliexpress products?? – This guide is for you. Know about Aliexpress product scraper and more about scraping Ecommerce websites.
Aliexpress is a giant ecommerce site based in China. Small businesses within and around China make this website. It contains lots of products, which are available to international buyers who wish to make transactions online.
Whether for personal, business, or research purposes, AliExpress contains lots of products and product details, which may be useful. It helps in market trend analysis, price comparison, consumer behavior analysis, sentiment analysis, knowing sales offer and strategy, ship dropping.
Most individuals, business owners, and researchers seek ways to extract these products and their details from Aliexpress. While there are various ways for scraping Aliexpress products, they may not be the best ways.
For instance, you may decide to manually extract (copy and paste) these product details from Aliexpress but this is time consuming and never a wise choice for smart individuals. Brilliant individuals may visit GitHub to download javascript or python Aliexpress crawlers, but this requires a level of coding and programming, which can go messy for individuals without adequate knowledge on programming.
So, the best way to extract information easily, efficiently, and timely from AliExpress requires the use of Aliexpress product scraper. This smart tool can scrape Aliexpress products without writing any code. So experienced and inexperienced both users can use it easily.

It Is Possible To Grab Following Data Points By Scraping Aliexpress:

Product name, product descriptions, pricing of products, product images, product variants, sellers’ contact information, shipping details, categories with structure, and customers’ reviews and ratings, etc. Know more about Aliexpress scraper and its features.
This Aliexpress product scraper tool is developed by professional Aliexpress data scraping services to extract Aliexpress products for their clients.
Why should you waste so much time extracting products manually on Aliexpress or trying codes you don’t understand when you can hire an Aliexpress scraping service at an affordable rate?
Depending on your need, Aliexpress scraping service can save the extracted data in various formats, such as CSV, Excel, Txt, XML, JSON, HTML, etc.
If you’re looking for the best Aliexpress product scraping services to help you handle your Aliexpress product scraping needs in the most effective manner, contact us today for a free quote. We are the experts in scraping Ecommerce websites like Amazon, Ebay, Walmart, Bigbasket and more in various countries. Check our data scraping work sample on our portfolio and get better understanding about our work.

Monday, January 20, 2020

How to Collect Company’s Information from Kompass ?

Business Directory Scraping

Only solution is Kompass scraper further more we can say Business directory scraping.
Kompass is a large database of companies’ information and contact details. This makes Kompass one of the leading business directories, a very good source of business data, and a platform where B2B and B2C business owners can find and communicate with prospects. It is available in various countries and contains a huge amount of business data.

Our Kompass Scraper Extracts:

  • About the company
  • Activity
  • Business location and address
  • Business name
  • URL
  • Category
  • Contact no
  • Employee
  • Executive name
  • Executive position
  • Export
  • Email addresses
  • Website
  • Year of established
Above all, specific field can be extracted.
For any business owner, it is always a great challenge to find marketing lists and the right prospects. Whether the business data is needed for direct mail campaigns, events, digital marketing, email, or telesales. Therefore, it is important to have access to up-to-date and accurate company information and contact details.

Advantage of Business Directory Scraping:

However, manually extracting a large amount of these business data from Kompass is nothing but a waste of time. The smartest and best way to go about collecting the company’s information from Kompass is using Kompass scraper or any other business directory data scraping service.
With Kompass data scraping, you would get an up-to-date and accurate company’s information for over 33 million companies and contacts of over 35 million business executives. You can also decide to focus your data scraping on a specific data field using Kompass data scraping services.
Kompass scraper not only collect the company’s information but also refine, clean, and store the data into a well-structured format. Kompass scraper is a powerful tool that helps business owners to find business leads and prospects from Kompass classifieds by locations and categories. Data scraping services enable business owners to get millions of business leads within their fingertips. Above all, the benefits that leads to Business directory scraping.
In other words, web scraping services is always at top priority by business owners.
One of the best and affordable Kompass data scraping service providers is Infovium web scraping scraping services. However, you can check sample file of Kompass data scraping. Infovium offers a wide range of business directory scraping solutions using powerful automated tools. Similarly, We provide Yellow pages scraperYelp data scraping, White pages scraping, Bloomberg data scraping, Yell data scraping, Owler data scraping, Hoovers data scraping, Justdial data scraping and more. In addition, we extract data from social networking sites, Ecommerce websites, Real estate sites and more.Contact us today.

Thursday, January 16, 2020

How to Scrape Product Data from Amazon using C-Sharp?




This Tutorial will explain you how we can extract product data from amazon.com using C sharp amazon scraper

Have you heard about amazon Data Scraping ?? It is a way to Scrape amazon Products data from amazon.com by Automated way using C Sharp.
amazon data scraper provides updated product information along with changing prices, reviews ,and more..

We can perform amazon data scraping and Extract Following Data using C Sharp amazon data scraper.

  • Product title
  • URL
  • ASIN
  • UPC
  • Item Model Number
  • No Of Reviews
  • Sales Rank Final
  • No Of Ratings
  • Product Dimensions
  • Best Seller Rank
  • Shipping Weight
  • Category
  • Price

how to scrape data from amazon using c sharp ?

Screen shot  from data will be extracting

Inspecting element for data extractions from amazon.com
To find appropriate data from website first we have to  inspecting and understanding html tag  which is associated with given data ..
please follow below steps to finding tags
  • Open browser (Google Chrome , Mozilla )
  • Copy and paste url you want to scrape.

Press F12 to view HTML structure of given site

  • find html tags for  require data and implement in C Sharp coding

C-Sharp Code to Scrape amazon.com
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.IO;
using System.Linq;
using System.Net;
using HtmlAgilityPack;
using Newtonsoft.Json;
using Newtonsoft.Json.Linq;
namespace amazon
{
class Program
{
/// <summary>
/// Store Data to Json format
/// </summary>
/// <param name=”args”></param>
static void Main(string[] args)
{
string url = string.Empty;
string strHtml = string.Empty;
//Console.WriteLine(“Please Enter URL :- “);
Console.WriteLine(“Please enter url:”);
url = Console.ReadLine();
Console.WriteLine(“Fetch Data From URL {0} …”, url);
strHtml = GetRequest(url);
object result = DataParse(strHtml);
Console.WriteLine(“Result :”);
Console.WriteLine(JsonConvert.SerializeObject(result, Formatting.Indented));
Console.ReadLine();
}
/// Lib Reference
///  1 : using System.Net;
///  2 : using System.IO;
///  3 : using System.Text;
/// </summary>
/// <param name=”url”></param>
/// <returns></returns>
public static string GetRequest(string url)
{
string strhtml = String.Empty;
HttpWebRequest request = (HttpWebRequest)WebRequest.Create(url);
request.AutomaticDecompression = DecompressionMethods.GZip;
request.UserAgent =
“Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36″;
using (HttpWebResponse response = (HttpWebResponse)request.GetResponse())
using (Stream stream = response.GetResponseStream())
using (StreamReader reader = new StreamReader(stream))
{
strhtml = reader.ReadToEnd();
}
return strhtml;
}
/// <summary>
/// This method is use to parse data from string
/// Return object with data
/// Lib Reference
///  1 : using HtmlAgilityPack;
///  2 : using Newtonsoft.Json;
///  3 : using Newtonsoft.Json.linq;
/// </summary>
/// <param name=”strHtml”></param>
/// <returns></returns>
public static object DataParse(string strHtml)
{
string Asin = String.Empty;
string url = String.Empty;
string Upc = String.Empty;
string Itemmodelnumber = String.Empty;
string price = String.Empty;
//string Shippingcost = String.Empty;
//string availability = String.Empty;
string Bsr = String.Empty;
string Salesrankfinal = String.Empty;
string Noofreviews = String.Empty;
string Noofratings = String.Empty;
//string productdescription = String.Empty;
string Productdimensions = String.Empty;
string Shippingweight = String.Empty;
string category = String.Empty;
List<string> hours = new List<string>();
HtmlAgilityPack.HtmlDocument htmlDocument = new HtmlAgilityPack.HtmlDocument();
htmlDocument.LoadHtml(strHtml);
htmlDocument.DocumentNode.Descendants()
.Where(n => n.Name == “script” || n.Name == “style”)
.ToList()
.ForEach(n => n.Remove());
// strJson = htmlDocument.DocumentNode.SelectSingleNode(“//script[@type=’application/ld+json’]”).InnerText;
//JObject jObject = JObject.Parse(strJson);
Asin = htmlDocument.DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“ASIN:”)).FirstOrDefault().InnerText.Replace(“ASIN:”,””).Trim();
Upc = htmlDocument.DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“UPC:”)).FirstOrDefault().InnerText.Replace(“UPC:”, “”).Trim();
Itemmodelnumber = htmlDocument. DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“Item model number:”)).FirstOrDefault().InnerText.Replace(“Item model number:”, “”).Trim();
Noofreviews = htmlDocument.DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“Average Customer Review:”)).FirstOrDefault().SelectSingleNode(“.//span[@class = ‘a-size-small’]”).InnerText.Replace(“customer reviews”, “”).Trim();
Salesrankfinal = htmlDocument.DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“Amazon Best Sellers Rank:”)).FirstOrDefault().SelectSingleNode(“.//ul[@class = ‘zg_hrsr’]”).InnerText.Trim().Replace(“Amazon Best Sellers Rank:”, “”).Replace(“&nbsp;”,””).Replace(“&gt;”,””).Replace(“\n”,””).Trim();
//Noofratings =  htmlDocument.DocumentNode
//    .SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“customer reviews”)).FirstOrDefault().InnerText.Replace(“customer reviews”, “”).Replace(“,”, “”).Trim();
Noofratings = htmlDocument.DocumentNode.SelectSingleNode(“//span[@id = ‘acrPopover’]/span[1]/a/i[1]/span”)
.InnerText.Trim();
//productdescription = htmlDocument.DocumentNode
//.SelectSingleNode(“//div[@id=’productDescription’]/ul”).InnerText.Trim();
Productdimensions = htmlDocument.DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“Product Dimensions:”)).FirstOrDefault().InnerText.Replace(“Product Dimensions:”, “”).Replace(“; 1.6 ounces”, “”).Trim();
Shippingweight = htmlDocument.DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“Shipping Weight:”)).FirstOrDefault().InnerText.Replace(“Shipping Weight:”, “”).Trim().Replace(“(View shipping rates and policies)”, “”);
//Shippingweight = Productdimensions.Substring(Productdimensions.IndexOf(“;”)).Replace(“;”, “”);
category = htmlDocument.DocumentNode.SelectSingleNode(“//span[@id=’productTitle’]”).InnerText.Trim();
price = htmlDocument.DocumentNode.SelectSingleNode(“//span[@class=’a-color-price’]”).InnerText.Trim();
//Shippingcost = htmlDocument.DocumentNode.SelectSingleNode(“//span[@id=’ourprice_shippingmessage’]/span”).InnerText.Trim();
//availability = htmlDocument.DocumentNode.SelectSingleNode(“//span[@id=’availability’]”).InnerText.Trim();
Bsr = Salesrankfinal.Substring(Salesrankfinal.IndexOf(“#”)).Replace(“#”, “”);
Bsr = htmlDocument.DocumentNode.SelectNodes(“//div[@class=’content’]/ul/li”).ToList().Where(x => x.InnerText.Contains(“Amazon Best Sellers Rank:”)).FirstOrDefault().InnerText.Replace(“Amazon Best Sellers Rank:”, “”);
Bsr = Bsr.Substring(Bsr.IndexOf(“#”)).Replace(“#”,””);
Bsr = Bsr.Substring(0, Bsr.IndexOf(“(“));
url = htmlDocument.DocumentNode.SelectSingleNode(“//link[@rel = ‘canonical’]”).Attributes[“href”].Value;
return new
{
URL = url,
ASIN = Asin,
UPC = Upc,
ItemModelNumber = Itemmodelnumber,
NoofReviews = Noofreviews,
SalesrankFinal = Salesrankfinal,
NoofRatings = Noofratings,
//productDescription = productdescription,
ProductDimensions = Productdimensions,
//Availability = availability,
BSR = Bsr,
ShippingWeight = Shippingweight,
Category = category,
Price = price,
//ShippingCost = Shippingcost,
};
}
}
}
Above code is developed in C-sharp   so  To Run this Code you need to use Visual Studio .   Develop all packages in one folder with above code …
Output file :- JSon
Clarification :- This  code available in this tutorial is  only learning purpose . We are not responsible for how it is used and assume no liability for any detrimental usage of the source code. This code is only  use for knowledge expansion regarding programming field.. by this tutorial we are not encourage amazon scraping or web scraping but will help to understand scraping.. also we are not responsible to provide any support for this code .. user can modify for learning purpose..