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Learning Social Media Analytics with R

Learning Social Media Analytics with R

By : Sarkar, Karthik Ganapathy, Raghav Bali, Sharma
5 (4)
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Learning Social Media Analytics with R

Learning Social Media Analytics with R

5 (4)
By: Sarkar, Karthik Ganapathy, Raghav Bali, 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 (10 chapters)
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9
Index

Follower graph analysis

So far, we have analyzed Twitter and tweets to uncover some interesting insights using techniques and concepts of trend analysis and sentiment analysis. We've utilized different attributes of tweets, like creation time, location and even the text itself, to answer certain questions. In this section, we will touch upon Twitter's network aspects. #BraceYourSelves

A social network is a network or a graph at its core. In formal words, a social network is generally a graph representing its users as nodes (or vertices) linked to each other based on certain relationships called edges. Each social network has its own definition of these relationships. For this section, we will focus on Twitter's relationships and network in general.

In Twitter-verse as we all know, there are no friends! A friend relationship is usually a bidirectional relationship, that is, if A is a friend of B, then it is safe to say that B is also a friend of A (well, usually; see Facebook...

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