15 Tips for Using Data Science to Research LinkedIn Posts & Comments

15 Tips for Using Data Science to Research LinkedIn Posts & Comments

Linked is the world’s largest professional networking site as is covered over at runrex.com, and is, therefore, the social networking site for professionals and businesses. On the platform not only can you find profile information of businesses and companies, but also profile details of their employees too. Additionally, LinkedIn is a huge platform for job posting and hosting, which means that extensive job-related data can be found freely on the platform. The fact that LinkedIn holds a huge amount of data makes it very interesting to businesses and companies. If you are interested in the data, this article will look to highlight 15 tips for using data science to research LinkedIn posts and comments, allowing you to retrieve useful insights from all that data.

• LinkedIn’s official API

As is revealed in discussions on the same over at guttulus.com, just as is the case for the other social network platforms, there is an official LinkedIn API available to developers for larger-scale datasets that will allow you to access and make sense out of LinkedIn posts and comments, which you can access by checking out the platforms Developers page.

• Limitations of the official API

Just as is also the case for the other social network platforms, LinkedIn’s official API isn’t perfect and has several limitations attached to it. According to runrex.com, it is not very comprehensive which means that data analysts and data scientists won’t be able to access all the data they require for research. It also prevents the storing of content, using it for research, and the number of users you can access at a given time for the same.

• Scraping

Given the limitations of the official LinkedIn API mentioned above and covered in more detail over at guttulus.com, if you want to access large quantities of data on the platform, then the option to consider is scraping LinkedIn web pages, which is what most data scientists are opting for.

• Scraping LinkedIn is legal

The question that many people ask about scrapping on LinkedIn is if it is legal, and according to runrex.com it is. This was ratified by a recent court case on the matter and upheld by the Circuit Court of Appeals which ruled scraping on LinkedIn was legal given that people who make data publicly available on their profiles do not have a reasonable expectation of privacy for that data.

• Talent Insights

If you are not keen on scarping LinkedIn data yourself, then you have the option of LinkedIn’s insights platform, Talent Insights. This platform, launched in 2018, is aimed at delivering key insights into talent and markets, while still delivering data points intuitively.

• Limitations of Talent Insights

Given that LinkedIn provides an insights platform for companies and businesses to leverage, you may be wondering why some still prefer scraping data themselves. Well, this is because, as outlined over at guttulus.com, Talent Insights has several limitations. For instance, LinkedIn charges for the service, and some companies prefer to put their own talent on things. It also answers a specific set of questions, which means that those companies with different questions will not get the answers they are looking for.

• What is required to scrape data on LinkedIn?

From discussions on the same over at runrex.com, it is clear that R is a very useful tool when it comes to data scraping and analysis and can help you here. Additionally, you can also opt for Python or Selenium if you prefer, as these too can be very useful when looking to research LinkedIn posts and comments.

• R libraries required

If you choose to go down the R route, then it is important that you know which library you will need to get things started. As highlighted over at guttulus.com, the “rvest” package is what you need to download as it will help you scrape LinkedIn posts and comments for useful and insightful information.

• Proxies

If you are using a LinkedIn scraper and want to avoid being considered to be a bot, which will lead to you being automatically banned, then you should consider using proxies. This, as covered over at runrex.com, is something companies like HiQ do.

• Mimic human surfing

As pointed out above, the LinkedIn system will lock you out and deny you access if they feel like you are a bot, which is why you should also make sure that you mimic how humans surf the internet otherwise you will be locked out.

• The importance of Selenium

According to the gurus over at guttulus.com, Selenium accepts commands and sends them to a browser. It doesn’t need a special server to execute tests since it directly starts a browser instance and controls it, which means that if you are scraping LinkedIn with Python, then Selenium is a very important part of your operations.

• The importance of Beautiful Soup

If you just need to go through the HTML structure and don’t need to emulate user interaction, then a parsing library like Beautiful Soup is all you need to get the job done as is covered over at runrex.com. It helps when you are conducting huge scraping and are running code designed to run on multi-instances. Beautiful Soup is a Python library for pulling data out of HTML and XML files.

• Sentiment analysis

Even as you scrape LinkedIn posts and comments for useful insights, you should not forget to carry out sentiment analysis, which will tell you how users feel about a certain post or topic. They may react in a positive, negative, or neutral manner as discussed over at guttulus.com, which will give you an idea of how popular a given post is.

• Best LinkedIn scrapers

While there are many LinkedIn scrapers out there for you to consider, there are some which stand out from the rest. According to the gurus over at runrex.com, these include Octoparse, ScrapeStorm, Helium Scraper, just to mention a few, all of which come very highly recommended.

• AI and LinkedIn

AI and LinkedIn are intrinsically linked given the role of the former in matching employees to jobs and creating better employee-employer matches. When researching LinkedIn posts and comments, you can take advantage of this feature to build models that will help you discover employees or employers that closely match a given profile.

These are just some of the things to consider when it comes to using data science to research LinkedIn posts and comments, with more on the same to be found over at the excellent runrex.com and guttulus.com.