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15 Tips for Using Python to Research Facebook Posts & Comments

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15 Tips for Using Python to Research Facebook Posts & Comments

As the subject matter experts over at runrex.com are quick to point out, Facebook is the biggest and most popular social network site of them all, boasting over 2.6 billion monthly active users, with a staggering 1.7 billion-plus visiting the site daily. This means that it contains a lot of valuable data that can be useful in so many different ways to companies and businesses out there. However, if businesses are to take full advantage of this data, they will first need to extract useful insights from it, which is where Python comes in, and this article will look to highlight 15 tips for using Python to research Facebook posts and comments.

Before you start using Python to extract useful and actionable insights from Facebook posts and comments, you will first need to collect your data according to the subject matter experts over at guttulus.com. The data analysis done by Python requires data as the raw material of the entire process, which is why data gathering is the first point. Here, you will have 3 broad options available to you as highlighted in the following three tips.

One of the ways through which you can gather Facebook data for analysis with Python is with the help of web scraping tools, which is a topic covered in detail over at runrex.com. Tools such as ScrapeStorm and Octoparse will help you with this, although this method of data collection comes with its limitations, chief among which is the risk of getting locked out by Facebook for violating its policy on the same.

Another option available to you when looking to gather Facebook data for analysis according to the gurus over at guttulus.com is data from public sets. The key here is finding data that closely matches your parameters as far as your research project is concerned, which is not always easy.

Whenever you are looking to conduct some sort of research and analysis regarding data from a social networking platform, one of the options that are always available is using the site’s API, and Facebook is no exception. Facebook’s Graph API will give you another option to extract the data that you need for your analysis a explained over at runrex.com.

Now that you have your data, obtained through any of the sources listed above, we must introduce Python and list the packages that will help you conduct your research and analysis. These packages include requests, JSON, re, time, Beautiful Soup, collections, and logging.

If you don’t have one or all of the above Python packages, then you will have no option but download and install them. When doing so, according to the subject matter experts over at guttulus.com, you should ensure that you install them on a Python Virtual Environment for your project as it is the best practice in such a situation.

As is highlighted over at runrex.com, Facebook is loaded with JavaScript, and without the above packages, you won’t be able to do anything with Python beyond simple requests like GET and POST, which is why downloading and installing said packages is important as it allows you to render JavaScript.

From discussions on the same over at guttulus.com, it is important to note that the script you come up with will be receiving data from 2 different sources: a file containing profiles URLs and another one containing credentials from a Facebook account to allow for the login. Therefore, you will need to define a function that allows you to extract this data from JSON files and convert it to a Python object.

If you want to extract the useful insights that you are looking for after logging into Facebook, you will need to crawl the Facebook profile or page URL as explained over at runrex.com. This will allow you to extract the page or profile’s public posts.

You will also need to define the number of posts that you wish to extract for a given query. Depending on your source of data, you will have a limit to the number of posts you can extract, something you need to consider. It is good practice to define a variable for the number of posts or comments you wish to extract.

According to the subject matter experts over at guttulus.com, it is also important that you clean your data before analysis. This includes removing irrelevant words, incorrect grammar, slang, weblinks, and so forth. You don’t want these words to be read as usable words which could affect your results hence why you should have them removed.

In addition to the Python packages mentioned earlier, there are certain libraries that you will need if you are to research Facebook posts and comments using Python. As per discussions on the same over at runrex.com, they include urllib3, which you should download or install depending on whether you have it available or not.

It is also important to highlight some of the information you can extract with Python. According to guttulus.com, some of the information you can obtain includes comments, photos, videos, and other post media, as well as post URL.

You will also have several options available to you as far as visualization is concerned, as is covered in detail over at runrex.com. You can either choose to have results such as comments organized into a table or you could go for word clouds, which you can use to show the most common words in your Facebook data set for example.

Sentiment analysis will help you know whether certain content is perceived in a positive, negative, or neutral manner and you should also not fail to carry it out when researching your Facebook posts and comments with Python. This will help you monitor your brand reputation and even foresee and prevent PR crises among other benefits.

These are just some of the things to keep in mind when using Python to research Facebook posts and comments, and you can uncover more insights on this wide topic by checking out the excellent runrex.com and guttulus.com.

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