Learning Social Media Analytics with R, 1st Edition eBook

Delivery: Can be download immediately after purchasing. For new customer, we need process for verification from 30 mins to 12 hours.
Version: PDF/EPUB. If you need EPUB and MOBI Version, please contact us.
Compatible Devices: Can be read on any devices.

About This Book
A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data
Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms.
Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering.
Who This Book Is For
It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise.

What You Will Learn
Learn how to tap into data from diverse social media platforms using the R ecosystem
Use social media data to formulate and solve real-world problems
Analyze user social networks and communities using concepts from graph theory and network analysis
Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels
Understand the art of representing actionable insights with effective visualizations
Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on
Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more
In Detail
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.

Style and approach
This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

This is a digital product.

Learning Social Media Analytics with R 1st Edition is written by Raghav Bali, Dipanjan Sarkar, Tushar Sharma and published by Packt Publishing. The Digital and eTextbook ISBNs for Learning Social Media Analytics with R are 9781787125469, 1787125467 and the print ISBNs are 9781787127524, 1787127524.