Mastering Social Media Mining with R: Extract valuable data from your social media sites and make better business decisions using R

Ravindran, Sharan Kumar, Garg, Vikram

ISBN 10: 1784396311 / ISBN 13: 9781784396312

Editore: Packt Publishing, 2015

Riguardo questo articolo

Like New.

Codice articolo ERICA77317843963116

Dati bibliografici

• Titolo: Mastering Social Media Mining with R: Extract valuable data from your social media sites and make better business decisions using R
• Editore: Packt Publishing
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 1784396311
• ISBN 13: 9781784396312
• Rilegatura: Paperback
• Condizione: Like New
• Tipo di libro: book

Descrizione articolo

Riassunto

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.

L'autore

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.