Lesser Known Yet Effective Parsing Technology

Have you ever wondered how we started storing and maintaining data? Flash drives only became popular in the early 2000s. Nevertheless, the big data analytics market will reach $103 billion by 2024!  Parsing technologies are becoming increasingly relevant as we continue to generate megabytes of data in seconds.

The most popular applications of parsing technology are business analytics, price regulation, customer satisfaction index calculation, and much more. Let's take a detailed look at some of the lesser-known applications of parsing technology.

1. Artificial Intelligence and Machine Learning

If you are active on social media, you have undoubtedly heard this term many times. Everyone is studying data science, talking about it, or trying to convince you to sign up for their data science course. We all know what web data is—unstructured information that can be cleaned and used according to requirements.

What is data science and how does it benefit from parsing? In fact, data science is a combination of data inference, development of new algorithms, and data processing that helps solve problems previously considered unsolvable due to the lack of large datasets.

But how is such a quantity of data created and where can it be found? Mostly these datasets belong to large corporations, and they rarely provide their datasets for free for research purposes. However, most of the data is present on their websites, albeit not in a structured format. This is where parsing technology comes to the rescue. A web parser is used in most data science projects to help gather more and more data on topics.

Most data scientists focus on algorithm development, while data engineers handle infrastructure requirements, so a person with parsing experience also becomes important.

Although hearing this word might make you think it's simply about collecting data from websites via parsing, parsing is more about cleaning and structuring the obtained data. Thus, it involves various skills, and due to recent changes in front-end development, these 'data collectors' must upgrade their skills daily.

2. Sentiment Recognition

This method is mainly carried out by collecting data from social networks with comment sections. Today, technology can tell with good accuracy whether a photo you uploaded is a cat or a dog.

But can it, on election day, tell with at least moderate accuracy which candidate will win by analyzing people's sentiment through their posts? It doesn't even have to be a direct mention or the candidate's name. Sentiment recognition algorithms pick up on hints and identify patterns that go beyond even the tweet itself.

It can draw conclusions using your location or the phone from which you wrote the message. This is one branch of machine learning that would be useless, and all research would stop, if not for web scraping. Gone are the days when messages were grouped and logistic regression was performed based on emojis found in them or hashtags following them. Even the difference between passive and active speech is detected, and machines can infer your personality and character by analyzing your activity.

3. Drug Addiction Recovery Programs

You probably haven't heard about this. Starting with version Pie, Google's Android comes with a "digital health" feature. There are rumors that even Apple plans the same in its next iPhones and iPads.

After thoroughly examining websites and collecting data, both tech giants concluded that these small devices now have a negative impact on people's productivity, unlike previous years.

Since Google hosts applications, and most of us actually use Gmail or Google Chrome, Google can learn a lot.

It can prevent us from checking email every few seconds, it can show fewer ads that it knows we are more likely to click after using our mobile phone for a certain period of time.

It can block certain websites when it's time to sleep. It can actually study us by collecting data on the web pages we browse to automatically take steps to break addiction.

4. Improving Image Recognition Algorithms

SURF and SIFT were invented in 2006 and 2010 and continue to be the best algorithms used for finding similarities between images. However, the race is not over yet.

There is a hunt for an algorithm that will not just look at pixels but can also deduce something based on experience (data it has already seen). Images are easy to find, and they are often tagged, which helps you obtain a labeled dataset in no time. So whether you are trying to write your first algorithm to distinguish cats from dogs or run an algorithm to differentiate satellite images with and without wildfires, you can easily get data by taking it from the Internet.

The Internet is certainly the largest and virtually inexhaustible repository of images. And when it comes to images, the more you train, the closer you get to your machine discovering a pattern that no human brain can deduce.

5. Creating a Domain-Specific Search Engine

Effective data parsing algorithms have helped people crawl both indexed and non-indexed pages to create large repositories of domain-specific data.

Knowing well that with limited resources they cannot compete with Google or Microsoft, they decided to invest in areas where they excel or have a lot of knowledge and first-hand information, such as pharmaceuticals or cooking recipes.

These sites are very popular among people involved in these specific fields, and thousands add them to bookmarks. The websites have a list of sites they crawl to create a search engine. Why do people prefer it over Google or Bing? Well, Google or Bing throw out irrelevant results along with real ones (along with promoted sites), so people prefer to come to them with their domain-specific needs.

6. Research

Although research conjures up images of laboratories, equipment, huge machines, wires and cables, most research today is done on laptops and MacBooks.

Datasets are not always easily accessible, and if they are, they are not very reliable. Therefore, most research depends on parsing. If you are writing a thesis on modern art or trying to find all the latest scientific papers on reversing the effects of global warming, instead of manually Googling and spending hours, you can write down the main topic and key keywords that are important, and try to find all articles you can, ordered by time and date. This will really give you better results.

Thus, a web parser is not just about price wars and content generation. Most of the latest AI algorithms and machine learning models are trained on data collected via parsing. Parsing services are truly the only way to get ahead in the Big Data race.