Mastering Social Media Mining with R, by Sharan Kumar Ravindran, Vikram Garg
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Mastering Social Media Mining with R, by Sharan Kumar Ravindran, Vikram Garg
Free Ebook PDF Mastering Social Media Mining with R, by Sharan Kumar Ravindran, Vikram Garg
Extract valuable data from your social media sites and make better business decisions using R
About This Book
- Explore the social media APIs in R to capture data and tame it
- Employ the machine learning capabilities of R to gain optimal business value
- A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data
Who This Book Is For
If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful.
What You Will Learn
- Access APIs of popular social media sites and extract data
- Perform sentiment analysis and identify trending topics
- Measure CTR performance for social media campaigns
- Implement exploratory data analysis and correlation analysis
- Build a logistic regression model to detect spam messages
- Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations
- Develop recommendation systems using Collaborative Filtering and the Apriori algorithm
In Detail
With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data.
This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming.
With this handy guide, you will be ready to embark on your journey as an independent social media analyst.
Style and approach
This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.
Mastering Social Media Mining with R, by Sharan Kumar Ravindran, Vikram Garg- Amazon Sales Rank: #1183255 in Books
- Published on: 2015-09-23
- Released on: 2015-09-23
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .56" w x 7.50" l, .95 pounds
- Binding: Paperback
- 248 pages
About the Author
Sharan Kumar Ravindran
Sharan Kumar Ravindran is a data scientist with over five years of experience. He is currently working for a leading e-commerce company in India. His primary interests lie in statistics and machine learning, and he has worked with customers from Europe and the U.S. in the e-commerce and IoT domains. He holds an MBA degree with specialization in marketing and business analysis. He conducts workshops for Anna University to train their staff, research scholars, and volunteers in analytics. In addition to coauthoring Social Media Mining with R, he has also reviewed R Data Visualization Cookbook. He maintains a website, www.rsharankumar.com, with links to his social profiles and blog.
Vikram Garg
Vikram Garg ( @vikram_garg) is a senior analytical engineer at a Big Data organization. He is passionate about applying machine learning approaches to any given domain and creating technology to amplify human intelligence. He completed his graduation in computer science and electrical engineering from IIT, Delhi. When he is not solving hard problems, he can be found playing tennis or in a swimming pool.
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Most helpful customer reviews
0 of 0 people found the following review helpful. This is my favorite book on social media covering almost everything I need By TRUNGNHAN PHAN This is my favorite book on social media covering almost everything I need: getting authentication from the social website (Open Authorization), data preprocessing, data modeling, and sentiment analysis with examples from the most popular social media sites such as Twitter, Facebook, Instagram and GitHub.The best part is that it’s a concise and practical book guiding you step by step packed with code and datasets for almost every chapter in the book except chapter one which is just an introduction.
0 of 0 people found the following review helpful. Very well paced guide to mine data with R! By Shanmuga Bharathi The use cases that are provided for R programming are very useful and gives you an insight on how it can be applied to extract information from data that is available for free in the internet!If you are a beginner at data mining and would like to know more about the APIs that are available to tap information from available data, develop recommendation engines based on the trend and sentiment of online user base, this book is an amazing guide!Would strongly recommend this to my friends!
0 of 0 people found the following review helpful. Excellent intro to text mining By Innes As someone fairly proficient in R (for stats) but new to text mining, I found this to be an excellent introduction. The explanations are clear and the example code is easily copied and tweaked to be relevant to your own research questions. The book got me from 'completely ignorant' to being able to do useful and powerful topic analyses of twitter (etc.) feeds, within a short space of time. Highly recommended.
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