Mining and Analyzing Facebook Data

Use Python, Data Science and Natural Language Processing techniques to extract data and analyze your Facebook page!

Facebook is one of the most popular social networks in the world, which allows you to chat with friends, share messages, links, photos, and videos. Companies can create business pages to promote and sell products and services. On the other hand, users (or fans) can like and follow the pages to receive updates about the company. It is important that companies know how to use the data of this social network in their favor and Facebook provides an API (called Graph API) for extracting several types of information about your page, making it possible to apply Data Science techniques to extract important and interesting insights considering some metrics, such as: engagement, views, content distribution, clicks, and many others! Below you can see the main topics that will be implemented step by step in this course:

• Extract data from your Facebook page using the Graph API

• Extract and analyze several types of information, such as: basic page data, views, clicks, engagement, impressions and posts

• Aggregate page fans by language, city, country, age, and gender

• Find relationships between the number of likes and dislikes

• View important information about page engagement

• View the positive and negative actions of the page's fans

• Compare paid, organic and viral content impressions

• Use time series to predict the future number of page fans using ARIMA algorithm

• Use the Facebook Prophet tool to predict future page engagement

• Extract reactions to page posts, such as the number of likes per post

• Extract texts from posts and apply natural language processing techniques, such as the word cloud to view the most frequent terms

• Perform key-word search in the posts

• Extract texts from comments written by the fans of the page to apply sentiment analysis to check whether the comments are positive or negative

During the course, we will use the Python programming language and Google Colab, so it is not necessary to spend time installing softwares on your own machine.  You will be able to follow the course with a browser and an Internet connection! This is the best course if this is your first contact with social media data analysis!