Mastering Social Media Mining with R + nemokamas atvežimas! | knygos.lt

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About This BookExplore the social media APIs in R to capture data and tame itEmploy the machine learning capabilities of R to gain optimal business valueA hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media dataWho This Book Is ForIf 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 also find this book useful.What You Will LearnAccess the APIs of popular social media sites and extract data from themPerform sentiment analysis and identify trending topicsMeasure CTR performance for social media campaignsImplement exploratory data analysis and correlation analysisBuild a logistic regression model to detect spam messagesConstruct clusters of pictures using the K-means algorithm and identify popular personalities and destinationsDevelop recommendation systems using collaborative filtering and the Apriori algorithmIn Detail

With the increase in the number of users on the Web, the amount of content has increased substantially, bringing with it a need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages by providing readily available data extraction and transformation packages, making it easier to carry out your ETL tasks.

This book will teach you how powerful business cases are solved by applying machine learning techniques to social media data. You will learn about important 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 with R using the 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 of the implementation of various use cases using R programming.