#AMAZON DATA EXTRACTOR HOW TO#
#AMAZON DATA EXTRACTOR INSTALL#
#AMAZON DATA EXTRACTOR MANUAL#

Possible text values that the identifier XPath can contain.XPath of the web element that contains the identifier.Name that will be used for the above XPath in the template placeholder.XPath of the web element that contains data.So, for all XPaths that form the XPathPool, the following info gets saved: For example, the text вЂColorвЂ™ on the pages below identifies that the adjacent text gives the product's color. If we observe, the data to extract always has an identifier text which tells what the data is about. _xpathPool.AddXPathToPool(new XPathObject('ProductColor', XPathObject('ProductColor', we'll look at how with some validations, the XPathPool selects the best valid XPaths that can dynamically define the placeholders. This shows how the above xpaths are added to the pool: var _xpathPool = new XPathPool() Void AddXPathsToPool(IEnumerable xPathObjs) Void AddXPathToPool(XPathObject xPathObj) IEnumerable GetBestMatchingXPaths(string html) The following shows the IXPathPool interface: public interface IXPathPool An XPathPool, as the name suggests, is a pool of XPaths collected from multiple web pages. To assist in this dynamic selection of XPaths, we introduce the notion of XPathPool to this blog. This problem can be solved if the XPath for the placeholders can be defined dynamically. There can always be an additional HTML row before our placeholder element, thus making the XPath in the placeholder invalid. On these sites, the positioning of elements is dynamic in nature. However, with modern e-commerce sites, this cannot be guaranteed. The approach shown above works completely fine if all our webpages follow the same structure as the defined template, i.e., the web elements in all the webpages are at the same XPath as defined by the template placeholders. This, however, is not a full-proof solution, as we will see in the next section. To extract other data such as product name, ratings, etc., from the webpage, we can keep adding more placeholders using the AddPlaceHolder method and then perform the extraction to get the desired results. Var json = extractionResult.ToJsonString() Var page = await () Д®xtractor.AddPlaceHolder('price', extractionResult = extractor.Extract(page)

#AMAZON DATA EXTRACTOR HOW TO#

Input Stream: The text stream to the pages from which the data needs to be extracted.ДЇor example, the following shows part of a web page used as a template to extract price information of products on .ДЄnd the following code shows how to set up HtmlExtractor to extract this info from the webpage: var response = await Client.GetAsync(productUri).

Placeholders: These provide the XPath to the HTML elements from which the data needs to be extracted.

The placeholders are declared relative to this.

A template stream: The stream representing HTML content to be used as a template.

To create and use the HtmlExtractor, we primarily need 3 things:

You can read about the working of HtmlExtractor from this blog. For this blog, we will use the HtmlExtractor. Define the HTML ExtractorД¬1TextParser provides 3 different extractors, namely, Starts-After-Continues-Until, HTML, and Template-based. Downloading from this installer also gives you access to samples and other components.

#AMAZON DATA EXTRACTOR INSTALL#

Search for the вЂњC1.TextParserвЂќ package and install the same.Д¬1TextParser can also be downloaded and installed through the service components tile in the ComponentOne Control Panel. The simplest way to install C1TextParser is via NuGet. NET Standard library that can extract data in a structured format from various semi-structured sources such as emails, webpages, text files, etc. This blog will look at creating an easier solution to extract data from e-commerce sites using the C1TextParser library with as an example. This obviously is cumbersome and not a feasible solution. You can visit a page, note down the information needed, probably in an excel file, and then repeat the same for other pages.

#AMAZON DATA EXTRACTOR MANUAL#

The simplest way to do this is via manual collection. Collection and analysis of this data can play a vital role in your success story whether you are an e-commerce competitor, a product comparison portal, a market research firm, etc. These e-commerce sites contain tons of data such as products available, price, availability, ratings, customer reviews, etc.

While customer satisfaction is rising with these many options, another important element is rising exponentially, i.e., Data. A user can purchase almost anything and everything across the many available e-commerce platforms. Over the years, e-commerce has seen tremendous growth as an industry.

Amazon data extractor