Book description
Tap into the realm of social media and unleash the power of analytics for data-driven insights using R
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.
Table of contents
-
Learning Social Media Analytics with R
- Table of Contents
- Learning Social Media Analytics with R
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Preface
- 1. Getting Started with R and Social Media Analytics
- 2. Twitter – What's Happening with 140 Characters
-
3. Analyzing Social Networks and Brand Engagements with Facebook
- Accessing Facebook data
- Analyzing your personal social network
- Analyzing an English football social network
-
Analyzing English Football Club's brand page engagements
- Getting the data
- Curating the data
- Visualizing post counts per page
- Visualizing post counts by post type per page
- Visualizing average likes by post type per page
- Visualizing average shares by post type per page
- Visualizing page engagement over time
- Visualizing user engagement with page over time
- Trending posts by user likes per page
- Trending posts by user shares per page
- Top influential users on popular page posts
- Summary
- 4. Foursquare – Are You Checked in Yet?
-
5. Analyzing Software Collaboration Trends I – Social Coding with GitHub
- Environment setup
- Understanding GitHub
- Accessing GitHub data
- Analyzing repository activity
- Analyzing repository trends
-
Analyzing language trends
- Visualizing top trending languages
- Visualizing top trending languages over time
- Analyzing languages with the most open issues
- Analyzing languages with the most open issues over time
- Analyzing languages with the most helpful repositories
- Analyzing languages with the highest popularity score
- Analyzing language correlations
- Analyzing user trends
- Visualizing top contributing users
- Analyzing user activity metrics
- Summary
- 6. Analyzing Software Collaboration Trends II - Answering Your Questions with StackExchange
- 7. Believe What You See – Flickr Data Analysis
- 8. News – The Collective Social Media!
- Index
Product information
- Title: Learning Social Media Analytics with R
- Author(s):
- Release date: May 2017
- Publisher(s): Packt Publishing
- ISBN: 9781787127524
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