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

Named entity recognition

Now, we arrive at another important concept called the named entity recognition, which aims to sort textual content into default categories such as the names of persons, organizations, locations, expressions of time, quantities, monetary values, and so on. The process is also known as entity identification, entity chunking, or entity extraction. This is a very powerful technique to understand large chunks of textual content in an automated manner.

Here, we will use an open source module to demonstrate the concept called Stanford NER (named entity recognizer), which is a widely used and one of the most popular named entity recognition tools. As Stanford NER is implemented in Java, we'll use the NLTK library, which provides an interface of Stanford NER to be used using Python.

The download is a zipped file (mainly consisting of classifiers). After unpacking, we have all needed files for running under Windows or Unix/Linux/macOS, a simple GUI, and the ability to run as...