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15 Tips for Using Python to Research LinkedIn Posts & Comments

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15 Tips for Using Python to Research LinkedIn Posts & Comments

As the world’s largest professional social networking platform as discussed over at runrex.com, LinkedIn is a great source of data that brands and organizations can use to their benefit. However, to leverage this data, you have to first gather the data for analysis. This is where Python and web scrapping comes in, and this article will look to highlight 15 tips for using Python to research LinkedIn posts and comments.

While LinkedIn provides an official API to developers as is revealed over at guttulus.com, it has several limitations. This includes the fact that it is not that comprehensive and as such, you won’t be able to access all the data you may require. This is why those looking to research LinkedIn posts and comments are opting for scraping of LinkedIn web pages.

You may remember the court battle between HiQ Labs and LinkedIn, which the former eventually won as discussed over at runrex.com. What came out of this court battle is that scraping on LinkedIn is legal, when done per LinkedIn’s terms and conditions. However, LinkedIn has since made it very difficult for web scraping tools as a result of the judgment.

As the subject matter experts over at guttulus.com are quick to point out, Python is the perfect language for web scraping. This is because of its many libraries that help with this technique, all of which are available to be downloaded and installed through the Python package manager pip.

If you don’t have Python installed, then the first thing to do is make sure you do so. Once this is done, you will need to install Python Requests, which is one of the important libraries mentioned in the point above. Requests will allow you to send HTTP requests using Python as described over at runrex.com and is very easy to install using pip.

Given that you will be using Chrome as your browser when scraping LinkedIn webpages, you will also need to download and install ChromeDriver. This is a separate executable used by WebDriver to control Chrome.

Another prerequisite needed to research LinkedIn posts and comments with Python is Selenium, which is a tool for writing automated tests for web applications. According to guttulus.com, Selenium is one of the best web browser drivers available for Python and is also easy to download and install as you can also do so using pip.

It is also important to note that the number of webpages that you can scrape on LinkedIn is limited, as is covered in detail over at runrex.com. Therefore, when scraping LinkedIn webpages with Python, it is a good policy to focus on the key data points as far as your project and objectives are concerned to ensure that you are focusing on what matters to you.

When scraping LinkedIn webpages with Python, you want to get as much information as possible, which is why you must access LinkedIn user profiles. However, to do so, you will need to be logged into a LinkedIn account as is revealed in discussions on the same over at guttulus.com.

You want to automate the process of logging into a LinkedIn account when scraping with Python, which is where ipython comes in. As covered over at runrex.com, it is an interactive shell built with Python and it offers different features including proper indentation and syntax highlighting and will be used to execute and test each command as you go, rather than having to execute a .py file.

If you successfully logged into your LinkedIn account, you should navigate back to Google to perform a specific search query to search for LinkedIn profiles on Google. To do this, you should use the “name=‘q” attribute which will allow you to locate the search form, and then you should extract the green URLs of each LinkedIn user profile.

When searching LinkedIn profiles on Google, once you test the results within ipython, you will find that some advertisements are being extracted as well. To avoid extracting unwanted advertisements, the gurus over at guttulus.com recommend only specifying “iUh30” class to ensure that you only extract LinkedIn profile URLs.

It is also important that you assign the LinkedIn URLs, (“linkedin_urls”), variable to equal the list comprehension, which contains a For Loop that will unpack each value and extract the text for each element in the list as explained over at runrex.com. Once done you can then use it to return the full list contents or specific elements within your list.

The important part of scraping LinkedIn webpages is scraping data, which is what will allow you to analyze posts and comments. To scrape data points from a web page, you will need Parsel, which, as described over at guttulus.com, is a library for extracting data points from websites. You will, therefore, need to have installed this library.

With Parsel installed and imported within your ipython terminal, it is also important to know how you go about extracting data from a LinkedIn account. According to discussions on the same over at runrex.com, the key is to ensure that you navigate to one of the Profile URLs returned from your search within the ipython terminal, and not through the browser.

As already mentioned earlier, due to LinkedIn’s limit on scraping, you will need to focus on key data points. To do so, you will need to use the Inspector Element on the webpage to locate the HTML markup you need to correctly extract each data point that is key for you.

The above discussion only just scratches the surface as far as this topic is concerned, and you can glean more insights on the same by checking out the excellent runrex.com and guttulus.com.

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