Web scraping is the extraction of unstructured data from website pages, social networks, and online stores. It is based on the use of computer algorithms and programs for automated access to web pages and extraction of the necessary information. Web scraping can be used to analyze competitors, monitor prices, study public opinion, etc.

How explain https://socleads.com/ parsing is the syntactic analysis of structured and unstructured data, such as code, text documents, etc. Using a parsing algorithm, input data is analyzed and converted into structured data, for example, for machine learning applications, databases. It is performed using automatic tools, parsers, or analyzers.

Most often, both processes are combined and simply called parsing. This is an important stage in preparing datasets for model training:

• The parser can extract different types of data, such as text, images, or numeric values.

• The data is then combined and converted into a convenient format.

• They are brought into line with the project requirements: unnecessary ones are removed, missing values ​​are filled in.

As a result, the machine learning model receives higher-quality and more complete data, which enables it to work accurately and reliably.

For example, Chisel AI’s natural language processing (NLP) and machine learning (ML) software, developed for insurance companies, extracts, interprets, classifies, and analyzes unstructured data in policies, quotes, and other documents 400 times faster than a human can, and with significantly greater accuracy.

How is data scraping used for business purposes?

Let's take the e-commerce sphere as an example. Using automatic data parsing, you can collect information about competitors' products on their pages and analyze important parameters:

• prices;

• assortment;

• discount policy;

• description and photos of goods.

This way, the seller will always be aware of the competitors' actions and will be able to promptly adapt to them in order not to lose customers. In addition, parsing makes it easy to collect reviews and messages mentioning the brand or products on various platforms in order to identify and fix problems, understand consumer preferences and predict demand for products.

Data parsing algorithms:

1. Regular expressions (regex). Used to describe patterns of text information. These patterns can be used to extract specific data from text files. For example, when collecting a database of customer email addresses for email newsletters. Suppose that customer information is stored in CSV format, the text is presented as a table, and one of the columns contains email addresses. Using the Python programming language, you can use the re module to find email addresses using a pattern specified with a regular expression.

2. This is a library for parsing HTML and XM files, extracting information from web pages. For example, we need to extract news headlines from the main page of a news site. To do this, first load the page using the requests library, and then use the BeautifulSoup object to parse the page's HTML code. Next, find all the news headlines using the find_all method and print the text of each headline.

3. This is a Python web scraping framework that can extract data from web pages using various methods, such as XPath and CSS selectors. For example, we need to scrape all the article titles from the main blog page. To do this, we create a spider to parse the data, specify the start address, and run the parse method, which extracts the article titles. Once the parsing is complete, you can save the resulting data to a file in JSON format.

4. Optical Character Recognition (OCR). The tool extracts text from images, PDF files. One example of use: recognizing text on banking documents, such as IBAN numbers on photographs of bank statements, to use them in payment processing systems.

5. Deep learning. A machine learning method using neural networks that self-learn on large datasets. One example of using deep learning methods for data parsing is Natural Language Processing (NLP). For example, neural networks can be trained to extract meaning from text data, determine sentiment, etc.

What data can be parsed?

You can collect information that is publicly available. For example, data from social media: posts on social networks, comments, metadata of user profiles. Often, they collect data from contextual advertising and e-commerce: search query history, purchase data, user reviews for personalized advertising and recommendation systems. You can also parse audio and video data for speech recognition, music genre classification, object recognition, image data: photographs, screenshots, medical images and much more.

Of course, the examples listed are not exhaustive, and everything will depend on the specific problem that needs to be solved.