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

Sentiment analysis

Sentiment analysis involves classifying comments or opinions in text into categories such as "positive" or "negative" often with an implicit category of "neutral". A classic sentiment application would be tracking what people think about different topics. Sentiment analysis in data science and machine learning is also called "opinion mining" or in marketing terminology "voice of the customer". It can be a very useful tool to check the affinity to brands, products, or domains. Sentiment analysis is extremely useful in social media monitoring as it allows us to get an overview of the wider public opinion behind specific topics.

Sentiment analysis also has its limitations and is not to be used as a 100% accurate marker.

As natural language can be very ambiguous with multiple connotations, it's hard if not impossible for machines and algorithms to detect them all. Sentiment analysis basically analyses patterns of words in phrases that are more likely to be positive, negative...