By Richard Heimann, Nathan Danneman

The expansion of social media during the last decade has revolutionized the best way members have interaction and industries behavior company. contributors produce facts at an unparalleled expense through interacting, sharing, and eating content material via social media. despite the fact that, examining this ever-growing pile of information is kind of difficult and, if performed erroneously, may lead to fallacious inferences.

By utilizing this crucial consultant, you'll achieve hands-on adventure with producing insights from social media info. This ebook offers targeted directions on the best way to receive, technique, and research various socially-generated information whereas supplying a theoretical history that can assist you effectively interpret your findings. you'll be proven R code and examples of knowledge that may be used as a springboard as you get the opportunity to adopt your individual analyses of commercial, social, or political data.

The e-book starts off via introducing you to the subject of social media information, together with its resources and homes. It then explains the fundamentals of R programming in an easy, unassuming approach. Thereafter, you may be made conscious of the inferential hazards linked to social media facts and the way to prevent them, prior to describing and imposing a set of social media mining techniques.

Social Media Mining in R presents a mild theoretical heritage, finished guide, and cutting-edge recommendations, and through interpreting this publication, you can be good built to embark by yourself analyses of social media information.

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000 R has many built-in functions for fitting statistical models. For example, we can estimate a linear regression model, that is, a model that predicts the level of a continuous variable with another continuous variable(s), by ordinary least squares (OLS) with the first two lines of the next code. Note that the tilde (~) in the following code is used to separate the left-hand side of the equation from the right-hand side of the equation. In this simple regression example, we are regressing y on x, or gre (mydata$gre) on gpa (mydata$gpa).

Instructions for downloading packages can be found in Chapter 2, Getting Started with R, but in general, installing packages is done by invoking install. packages("…"). packages("twitteR") > library(twitteR) Now, we are just a few lines of R code away from pulling in Twitter data. If you are using a Windows machine, there is an additional prestep of downloading a cecert. pem") [ 34 ] Chapter 3 In this example, we have saved the file to the C: directory, but you can save it to wherever you have the appropriate permissions on your machine.

Packages are provided by a robust academic and practitioner community and are available for inclusion through simple downloads. • Does the software fully support programming versus point-and-click? °° Users can utilize R as an interactive programming language or a scripting language. There are also packages, such as Rcmdr, that allow limited point-and-click functionality. • Are the visualization options adequate for your needs? °° R has a very powerful, simple-to-use suite of graphical capabilities.

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