
Extract and analyze Facebook data using Python and the Facebook API to gain insights on page metrics, engagement, and content performance across three course sections.
Execute the plan of attack for Facebook data analysis by extracting basic page data, obtaining access tokens, and graphing followers, engagement, likes and dislikes, and demographics to guide marketing.
Extract basic profile data from a Facebook page using the Graph API, with a permanence based token and page ID, retrieving fields such as name, category, about, and website.
Analyze paid and unpaid likes by comparing bait and no-bait engagement, visualize page likes over time, and explore related metrics in a data frame.
Explore how page impressions distribute across paid, organic, and viral channels, compare them with normalization and visuals, and preview time series and machine learning insights.
Learn to work with time series data by installing libraries, preparing a data frame with dates and metrics, and using arima to predict future fans and engagement.
Plan of attack guides analysts to extract reactions, video views, and post texts from Facebook pages, apply word clouds, keyword searches, and sentiment analysis for marketing insights.
Install and import the Google translator library to translate texts from Brazilian Portuguese to English. Create a new translation field in the data frame and translate each post.
Develop a text preprocessing function to clean English texts for NLP, including lowercasing, link removal, and stop-word punctuation filtering, and apply it to dataset to create a preprocessed translation column.
Extract comments from Facebook posts using graph connections, build a data frame of commands from messages, and prepare for preprocessing and translation in the next lecture.
Learn to pre-process Facebook comments by translating texts to English, cleaning non-text entries, and filtering by length to prepare data for sentiment analysis.
Recap techniques for extracting Facebook page data, generating visualizations of cliques, engagements, and impressions, using the Showtime series algorithm to predict future engagement, and analyzing posts with natural language processing.
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!