A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world.
Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.
In statistical analysis, R is now the de facto standard, at least at many universities. You can readily find texts explaining how to use R for general purposes. In contrast, the current book focuses on taking R for social media mining. Chapter 2 is a too brief walkthrough of the syntax of R. I recommend if you are indeed new to R to find a lengthier text explaining R itself.
Chapter 3 is where the book gets into the specifics of social data extraction. Using Twitter as a case study. Nicely at least thus far, you can get a free developer account. Don't know how long Twitter will allow this. But even if you have to pay, it may still be worthwhile. But the chapter shows how you can rapidly go some preliminary analyses, aided crucially by functions prebuilt in R.
Another chapter warns of the shortcomings of trying to quantify sentiment from such sources as a Twitter data feed. There are certain features unique to Twitter. The 140 character hard limit means that writers are forced to concentrate on the message. Less verbosity than in a blog or other type of web page. The text also introduces you to the concept [and indeed the necessity] of a lexicon. And of using Bayes classifiers. The book really just touches on a vast topic. But hopefully you get the gist of how R can do this.
Overview
A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world.
Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will ...