Learning Social Media Analytics with R : Tap Into the Realm of Social Media and Unleash the Power of Analytics for Data-driven Insights Using R

Tap into the realm of social media and unleash the power of analytics for data-driven insights using RAbout This BookA practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media dataLearn 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 ForIt 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 LearnLearn how to tap into data from diverse social media platforms using the R ecosystemUse social media data to formulate and solve real-world problemsAnalyze user social networks and communities using concepts from graph theory and network analysisLearn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channelsUnderstand the art of representing actionable insights with effective visualizationsAnalyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so onLearn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many moreIn DetailThe 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 approachThis 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. Cover ......Page 1 Copyright......Page 3 Credits......Page 4 About the Author......Page 5 About the Reviewer......Page 8 www.PacktPub.com......Page 9 Customer Feedback......Page 10 Table of Contents......Page 12 Preface......Page 20 Chapter 1: Getting Started with R and Social Media Analytics......Page 26 Understanding social media......Page 27 Advantages and significance......Page 29 Disadvantages and pitfalls......Page 31 Social media analytics......Page 32 A typical social media analytics workflow......Page 33 Data processing and normalization......Page 34 Data analysis......Page 35 Opportunities......Page 36 Challenges......Page 37 Getting started with R......Page 38 Environment setup......Page 39 Data types......Page 41 Vectors......Page 43 Arrays......Page 45 Matrices......Page 46 Lists......Page 47 DataFrames......Page 49 Built-in functions......Page 51 User-defined functions......Page 52 Looping constructs......Page 53 Conditional constructs......Page 54 apply......Page 56 sapply......Page 58 tapply......Page 59 mapply......Page 60 Visualizing data......Page 61 Getting help......Page 63 Data analytics......Page 64 Analytics workflow......Page 65 Machine learning techniques......Page 67 Supervised learning......Page 68 Text analytics......Page 69 Summary......Page 70 Chapter 2: Twitter – What's Happening with 140 Characters......Page 72 Understanding Twitter......Page 73 APIs......Page 74 Registering an application......Page 75 Connecting to Twitter using R......Page 78 Extracting sample Tweets......Page 80 Trend analysis......Page 81 Sentiment analysis......Page 91 Sentiment polarity......Page 92 Features......Page 93 Sentiment analysis in R......Page 94 Follower graph analysis......Page 104 Challenges......Page 111 Summary......Page 112 Chapter 3: Analyzing Social Networks and Brand Engagements with Facebook......Page 114 Understanding the Graph API......Page 116 Understanding Rfacebook......Page 119 Data access challenges......Page 120 Analyzing your personal social network......Page 121 Basic descriptive statistics......Page 122 Analyzing mutual interests......Page 125 Build your friend network graph......Page 127 Visualizing your friend network graph......Page 128 Analyzing node properties......Page 129 Degree......Page 130 Closeness......Page 132 Betweenness......Page 133 Cliques......Page 134 Communities......Page 135 Analyzing an English football social network......Page 139 Basic descriptive statistics......Page 141 Visualizing the network......Page 144 Analyzing network properties......Page 145 Page distances......Page 146 Density......Page 147 Coreness......Page 148 Analyzing node properties......Page 149 Closeness......Page 150 Betweenness......Page 151 Visualizing correlation among centrality measures......Page 152 Eigenvector centrality......Page 154 PageRank......Page 155 HITS authority score......Page 156 Page neighbours......Page 157 Cliques......Page 158 Communities......Page 159 Getting the data......Page 164 Curating the data......Page 165 Visualizing post counts per page......Page 166 Visualizing post counts by post type per page......Page 167 Visualizing average likes by post type per page......Page 168 Visualizing average shares by post type per page......Page 169 Visualizing page engagement over time......Page 170 Visualizing user engagement with page over time......Page 171 Trending posts by user likes per page......Page 173 Trending posts by user shares per page......Page 174 Top influential users on popular page posts......Page 175 Summary......Page 177 Chapter 4: Foursquare – Are You Checked in Yet?......Page 178 Foursquare – the app and data......Page 179 Foursquare APIs – show me the data......Page 180 Creating an application – let me in......Page 181 Data access – the twist in the story......Page 182 Getting category data – introduction to JSON parsing and data extraction......Page 183 Getting the data – the usual hurdle......Page 188 Getting data for a city – geometry to the rescue......Page 189 Analysis – the fun part......Page 192 Basic descriptive statistics – the usual......Page 193 Framing the recommendation problem......Page 199 Building our restaurant recommender......Page 200 Extracting tips data – the go to step......Page 205 The actual data......Page 207 Basic descriptive statistics......Page 208 The final rankings......Page 212 Venue graph – where do people go next?......Page 214 Challenges for Foursquare data analysis......Page 217 Summary......Page 218 Chapter 5: Analyzing Software Collaboration Trends I – Social Coding with GitHub......Page 220 Environment setup......Page 221 Understanding GitHub......Page 222 Using the rgithub package for data access......Page 225 Registering an application on GitHub......Page 226 Accessing data using the GitHub API......Page 228 Analyzing weekly commit frequency......Page 231 Analyzing commit frequency distribution versus day of the week......Page 233 Analyzing daily commit frequency......Page 235 Analyzing weekly commit frequency comparison......Page 236 Analyzing weekly code modification history......Page 238 Retrieving trending repositories......Page 240 Analyzing repository trends......Page 243 Analyzing trending repositories created over time......Page 244 Analyzing trending repositories updated over time......Page 246 Analyzing repository metrics......Page 248 Visualizing repository metric distributions......Page 250 Analyzing repository metric correlations......Page 251 Analyzing relationship between stargazer and repository counts......Page 253 Analyzing relationship between stargazer and fork counts......Page 254 Analyzing relationship between total forks, repository count, and health......Page 257 Visualizing top trending languages......Page 258 Visualizing top trending languages over time......Page 260 Analyzing languages with the most open issues......Page 262 Analyzing languages with the most open issues over time......Page 263 Analyzing languages with the most helpful repositories......Page 265 Analyzing languages with the highest popularity score......Page 267 Analyzing language correlations......Page 269 Visualizing top contributing users......Page 272 Analyzing user activity metrics......Page 274 Summary......Page 278 Chapter 6: Analyzing Software Collaboration Trends II - Answering Your Questions with StackExchange......Page 280 Understanding StackExchange......Page 281 Data access......Page 282 The StackExchange data dump......Page 283 Contents of data dumps......Page 284 Quick overview of the data in data dumps......Page 285 Getting started with data dumps......Page 289 Data Science and StackExchange......Page 290 Demographics and data science......Page 299 Challenges......Page 305 Summary......Page 306 A Flickr-ing world......Page 308 Creating the Flickr app......Page 310 Connecting to R......Page 313 Getting started with Flickr data......Page 316 Understanding Flickr data......Page 317 Understanding more about EXIF......Page 318 Understanding interestingness – similarities......Page 326 Elbow method......Page 327 Silhouette method......Page 328 Preparing the data......Page 335 Building the classifier......Page 339 Challenges......Page 342 Summary......Page 343 Chapter 8: News – The Collective Social Media!......Page 344 News data – news is everywhere......Page 345 Accessing news data......Page 346 Creating applications for data access......Page 347 Data extraction – not just an API call......Page 348 The API call and JSON monster......Page 349 Sentiment trend analysis......Page 356 Getting the data – not again......Page 357 Basic descriptive statistics – the usual......Page 358 Numerical sentiment trends......Page 361 Emotion-based sentiment trends......Page 364 Topic modeling......Page 368 Getting to the data......Page 369 Basic descriptive analysis......Page 370 Topic modeling for Mr. Trump's phases......Page 373 Pre-processing the data......Page 374 The modeling part......Page 375 Analysis of topics......Page 376 Summarizing news articles......Page 378 Understanding LexRank......Page 379 Summarizing articles with lexRankr......Page 380 Challenges to news data analysis......Page 385 Summary......Page 386 Index......Page 388 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 r..