Book Image

Learning Social Media Analytics with R

By : Dipanjan Sarkar, Karthik Ganapathy, Raghav Bali, Tushar Sharma
Book Image

Learning Social Media Analytics with R

By: Dipanjan Sarkar, Karthik Ganapathy, Raghav Bali, Tushar Sharma

Overview of this book

The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.
Table of Contents (16 chapters)
Learning Social Media Analytics with R
About the Author
About the Reviewer
Customer Feedback

Text analytics

Text analytics is also often called text mining. This is basically the process of extracting and deriving meaningful patterns from textual data which can in turn be translated into actionable knowledge and insights. Text analytics consist of a collection of machine learning, natural language processing, linguistic, and statistical methods that can be leveraged to analyze text data. Machine-learning algorithms are built to work on numeric data in general, so extra processing and feature extraction and engineering is needed for text analytics to make regular machine learning and statistical methods work on unstructured data.

Natural language processing, popularly known as NLP, aids in doing this. NLP is defined as a specialized field in computer science and engineering and artificial intelligence which has its roots and origins in computational linguistics. Concepts and techniques from NLP are extremely useful and help in building applications and systems that enable interaction between machines and humans with the aid of natural language which is indeed a daunting task. Some of the main applications of NLP are:

  • Question-answering systems

  • Speech recognition

  • Machine translation

  • Text categorization and classification

  • Text summarization

We will be using several concepts from these when we analyze unstructured textual data from social media in the upcoming chapters.