Sentiment analysis and entity recognition are two powerful social media analytics techniques to get context around user content. Sports being a sentiment and emotion inciting subject among audiences, for this chapter the dataset we used were tweets using the Twitter API on the English Football Premier League. We used the Twitter REST and Streaming API to collect the data and also applied basic cleaning explained in Chapter 2,Harnessing Social Data - Connecting, Capturing, and Cleaning) and new cleaning methods such as device detection from Twitter API metadata. Sentiment Analysis allows us to categorize text into positive, negative, and neutral categories. We also learnt that there are limitations to sentiment analysis with accuracy, especially in ambiguous expressions. We used the VADER (Valence Aware Dictionary for Sentiment Reasoning) module from NLTK for sentiment analysis. We also saw that we can build our own sentiment analysis algorithm through machine learning on test and...
Python Social Media Analytics
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Python Social Media Analytics
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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.
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Current Title:
Python Social Media Analytics
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Introduction to the Latest Social Media Landscape and Importance
Harnessing Social Data - Connecting, Capturing, and Cleaning
Uncovering Brand Activity, Popularity, and Emotions on Facebook
Analyzing Twitter Using Sentiment Analysis and Entity Recognition
Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
The Next Great Technology – Trends Mining on GitHub
Scraping and Extracting Conversational Topics on Internet Forums
Demystifying Pinterest through Network Analysis of Users Interests
Social Data Analytics at Scale – Spark and Amazon Web Services
Customer Reviews