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

Customized sentiment analysis

As mentioned earlier, sentiment analysis is the process of identifying and extracting sentiment information related to a specified topic, domain, or entity, from a set of documents. The sentiment is identified using trained sentiment classifiers. Thus, the quality and the type of the training data have a big impact on the classifier's performance. Most pre-trained classifiers (like VADER) are trained on general texts because they are designed to be versatile for use on different topics. Unfortunately, when we need to extract sentiment from a specific textual data (for example, very domain specific) such as a general classifier might not perform very well. That is why, it makes great sense to train our own classifier that will fit specific needs, or alternately, just train a general classifier, but based on customized, verified, and known datasets. In short, the magnitude of adaptation to the domain is what makes the difference between a good sentiment analysis...