January 5

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An Overview on Scraping and Data Parsing


There are many areas where the web or data scraping is used. Web scraping automates the extraction process on the web in a fast and efficient way. Scraping is a process used in businesses for market research among other aspects.

For example, a store can compare its prices to competitors effortlessly. When you get data in the raw HTML format after the scraping process using a proxy, the data goes through a parser. The HTML is then converted into an easy-to-read and understood format.

Not so many people are familiar with the term data parsing. This article will answer the question, what is parsing? The simplest definition is that data parsing turns raw unstructured data into well-structured information.

Data parsing is analyzing text or string into syntactic elements with a program known as a parser that decomposes and transforms information into a readable format to process it further. Therefore, a data parser is a software program that executes the process and also analyzes tokens produced by a lexer. A lexer acts as an assistant to the parser.

The Relationship Between Scraping and Parsing

Web scraping commonly needs parsing to change the content with the irrelevant information into an understandable format to give the most accurate results. After web scraping, the next process is usually data parsing to extract and define the results after analysis.

For the scraping process to happen accurately and allow access to many homepages, companies should provide an API (Application Programming Interface). To make scraping easier and undetectable, scrapers need to be configured with a proxy to make each process unique. Companies such as Smartproxy provide a range of proxies to simplify the process.

How Does Data Parsing Work?

Parsing is an important part of web scraping. Data parsing is the process that transforms the snippets of code that have been scraped to a format that is easy to understand. A parser has also been known as a tokenizer or a proper parser.

It inspects and breaks down tokens or snippets of code for syntactic analysis. A parser produces a structured code known as a syntax tree. It is called a tree because of the many levels it has. 

There are two steps in the data parsing process: lexical analysis and syntactic analysis. The lexical analysis is the primary parsing step, which allocates data structures collected before changing the format. The syntactic analysis is when the allocated data is converted based on a pre-written code by the parser.

Types of Data Parsing

A parser is usually categorized into two types, which are top-down parsers and bottom-up parsers. Their difference is mainly in the way the parse tree is generated.

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    1. Top-Down Parsers

    A top-down parser generates data for the input string with the assistance of grammar productions. A top-down parser can be divided further into two other types: recursive descent and non-recursive descent parser.

    i) Recursive Descent or Brute Force Parser is also known as brute force parser or the backtracking parser. It uses brute or backtracking to generate the parse.

    ii) Non-recursive descent or Predictive Parser is also known as LL(1) parser or a predictive parser with no backtracking. It uses parsing tables and generates the parser tree instead of backtracking.

    2. Bottom-up Parser

    A bottom-up parser generates its parse tree for specific input strings that help grammar productions by compressing. It starts from non-terminals to the end of the start symbol. Bottom-up is classified into two types: the Operator Precedence parser and the LR parser.

    Operator Precedence Parser

    generates the parse tree from given strings and grammar in two consecutive terminals and epsilon. 

    LR Parser

    is the bottom-up parser that uses unambiguous grammar. It follows the reverse of the rightmost derivation.  LR parsers have four different types namely: LR(0),  SLR(1),  LALR(1) and  CLR(1).

    This may sound very complex, but if you use an existing web scraping tool such as ones provided by Octoparse, Parsehub, or similar you don’t have to worry about the parsing process too much. These specialized web scrapers already have built-in parsers that will convert the scraped data to the format you specify.

    Uses of Data Scraping

    Data scraping is an area whose influence can be seen, especially in business. It’s almost impossible, especially with the rise in competition for data, to find a place where data scraping is not valuable or important.

    1. Marketing and Sales

    It assists in finding sales leads. You can conduct market research by using public sources such as Twitter. Web scraping can help analyze people’s interests and monitor consumer reviews on different platforms.

    2.  Strategy Development

    Web scraping assists with giving good facts by allowing a one-time extraction for analysis to use in monitoring the strategy later. Scraping also allows you to develop a web crawler to check the news in the relevant area.

    3. Product Development

    Web scraping allows you to analyze customer reviews on rating platforms. This enhances product development by letting you know what products the customers need.

    4. Price & Competitor Analysis

    If you’re working on a price strategy, web scraping allows you to extract and check pricing and discounts from competitors. It also allows you to further track your competitor’s latest developments.

    Data scraping can also be used in other sectors. Scraping tools are commonly used in journalism, academic research, news, and reputation monitoring.

    Conclusion

    A combination of a good proxy and excellent web scraping tools can enhance your business strategy. Scraping gives any business access to information that they could use to increase sales revenue.

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