文件名称：Learning Social Media Analytics with R
文件大小：14.08MB
文件格式：AZW3
更新时间：2020-07-02 18:35:24
Social Media Analytics R
Learning Social Media Analytics with R by Dipanjan Sarkar English | 6 Jun. 2017 | ASIN: B071VMV25V | 394 Pages | AZW3 | 14.08 MB Key Features 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. Book Description The Internet has truly grown humongous especially in the last decade with the rise of various forms of social media, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the user to understand the current social media landscape and how analytics can be leveraged to derive insights from it. This data can be analyzed for gaining valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers in framing business problems and solving those 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, Facebook, and so on. It will also guide readers in establishing detailed workflows for processing, visualization, and analysis of data to transform social data into actionable insights. What you will learn Learn 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