Book Image

Python Social Media Analytics

By : Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk
Book Image

Python Social Media Analytics

By: Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk

Overview of this book

Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Detecting trends in time series

In previous sections, we have analyzed the most frequent keywords and phrases without taking into account the time frame. However, a brand can benefit from a temporal dimension and dynamic analysis of the content of posts and comments.

Our goal in this section is to analyze in time series the moments of highest engagement to posts in terms of likes and shares and then see what those posts were about.

Firstly, we convert a string with a date into a datetime object:

df_comments['date'] = df_comments['created_time'].apply(pd.to_datetime) 

The next operation transforms the data frame into a time series and creates an index on a datetime object:

df_comments_ts = df_comments.set_index(['date']) 

Finally, we subset our data frame to only get the verbatims since the beginning of 2015:

df_comments_ts = df_comments_ts['2015-01-01':] 

We have to execute the same operation on our data frame containing the post to be able to make comparisons:

df_posts['date'] = df_posts['created_time...