“To parse” means to analyze strings of symbols or data elements and map semantic relationships between them for sense-making. Practical parsing applications are the home ground for researchers, developers, and data scientists.
Conceptually, parsing is a complex set of methods in Formal Logic, Computer Science, and Linguistics. To keep it to the point, we focus specifically on the parsing involved in web scraping for business needs.
To understand parsing, you need to understand the difference between information and data. Parsing helps transform one into another.
Data is information with defined connections or syntax. The critical difference is that data allows us to draw inferences and perform logical operations.
For example, a list of names and another list of invoice totals are just two pieces of information, meaningless on their own. But once you connect each name with the corresponding number, they turn into your customer data.
“Extracting data” is the term you often encounter in parsing discussions. It refers to detecting specific pieces of information in large, messy sources and reorganizing them according to the rules set by the user.
The names and invoice amounts from the example above could be scattered across your accounting app, among hundreds of other data strings. Your parser found them and “copied” to a spreadsheet next to each other.
Here are some examples of the jobs parsers do:
Computer parsing is used whenever we utilize big data, once there is too much of it to organize it manually.
Parsing is used as a part of the compilation process to “translate” high-level code into the low-level machine language a CPU can understand and execute (except for interpretable languages, where the process is slightly different.)
We already mentioned parsing in the context of web scraping before. There, parsing is a specific stage in the web scraping workflow.
A short reminder: the parts of the web scraping workflow are crawling, scraping, parsing, mathematical operations, and feeding it to a database.
HTML is not easily interpretable by a human. When scrapers retrieve HTML files, parsers transform them into a clean and readable form: numerical data, text fields, images, and tables. They even make it searchable. Or they can skip the human-readable form and change it into a format suitable for an analytical tool.
Parsing is commonly used in NLP, AI, and ML tasks. Rules are not enough to learn the probability and manner of elements’ cooccurrence. Computers need many, many examples. Parsers extract that information from scraped files and feed it to the machine learning model. Eventually, the AI learns to associate the word “pug” and a picture of the breed.
An analysis is the main reason we parse information. Investment analysis, marketing, social media, search engine optimization, scientific studies analysis, stock markets… It is easier to name a discipline where parsing is not used.
Imagine trying to read all the news published on the web or adding stock prices to a spreadsheet by hand every day. Even if you can, it will take too long, and the information will become outdated before you finish collecting it.
Parsers detect relevant pieces of information, extract, and summarise them under categories for the analysts or intelligence specialists to review. Analysts can focus on thinking instead of trying to push through the clutter of raw data.
Manually collecting all the mentions about a specific subject, individual, or business would take far too long. A program, however, can scan the web, scrape all the mentions, and then parse only relevant pieces.
Google Knowledge graph is one example: when you search a name, you receive the resulting relevant URLs and a block with organized information about that person.
Intelligence or PR agencies regularly scrape social media for opinions related to their clients. Parsers organize it into a readable form and flag positive, negative, neutral, or extreme views. At the current scale of SMM, manual extraction is not practicable.
Fintech and legacy banks utilize “enriched context” to improve their risk assessment accuracy. It might include phone bills or current property values. Bank analysts can make more granular and contextual decisions without seeing the person (in an ideal world, anyway).
Parsed data can empower lead generation and personalized sales. Health struggle, marriage date, interests, purchase reviews, bills, education, travel history, event attendance, and awards become customer insights once parsed into a CRM.
Parsers can be used to create shipping labels. You fill out the online form and place the order. A parser reads it and arranges it into a slip, invoice, and instructions for the warehouse.
Good old grammar checkers that remind you when you forgot a comma or misspelled a word use parsing, too. They compare your input to a grammatical or statistical model, detect errors, and notify the user.
Parsers range from very simple to powered by an advanced AI. There is an immense number of parsers for most applications and languages. You can find ones for emails, CRM, customer data, HTML, big data, accounting apps, etc.
You can program your own data parser or purchase an existing tool. Neither is “good” or “bad.” They just fit different situations. When writing your own, you can use any language, including SQL.
A few things to remember when getting a data parser:
Like any ready-made tool, parsers and web scrapers you can purchase have their limitations. They are less flexible and serve most common tasks. Anything beyond that will need to be custom-built.
Advantages of building your own parser:
Disadvantages of building your own parser:
When scraping tasks involve only a few specific websites or trivial tasks, it might be cost-efficient to purchase a data parser or web scraping tool.
Advantages of purchasing a parser:
Downsides of purchasing a parser:
On top of ready-made tools, there are intermediate solutions like API or programming libraries. You will have to do manual coding, but it will be easier.
Parsing organizes “raw” information blocks into a structured and usable form. It uses relationship logic or rules (i.e., syntax) to connect its elements, making it “more digestible” for humans or other applications.
Parsing allows you to get more value from the data abundance by making the process more accessible and cost-efficient. When the big data has been parsed, we can analyze data and notice details that are hard to detect amongst the clutter.