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English | 2017 | ISBN: 1787127524 | 394 Pages | True PDF | 12 MB
Tap into the realm of social media and unleash the power of analytics for data-driven insights using R.
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
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.
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Learning Social Media Analytics with R.pdf
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