Text Mining is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages.
Our aim in this book is to provide you the information that you will use to develop a practical application from the concepts learned and you will understand how text mining can be leveraged to analyze the massively available data on social media.
We hope you'll get as much from reading this book as we did from writing it.
Chapter 1, Statistical Linguistics with R, covers the basics of statistical analysis, which forms the basis of computational linguistic. This chapter also discusses about various R packages for text mining and their utilities.
Chapter 2, Processing Text, intends to guide readers in handling textual data, right from scratch. Accessing the data from various sources, cleansing texts using Regular expressions, stop words, and help develop skills to process raw texts effectively using R language.
Chapter 3, Categorizing and Tagging Text, empowers the readers to categorize the texts into different word classes or lexical categories.
Chapter 4, Dimensionality Reduction, covers in detail, the various dimensionality reduction methods that can be applied on text data and extending the concept to extract contexts from data in the next chapter.
Chapter 5, Text summarization and Clustering, deals with text summarization and document clustering methods that can be applied to textual documents.
Chapter 6, Text Classification, deals with pattern recognition in text data, using classification mechanism. We will deal with statistical and mathematical aspects along with the implementation on public data sets using R language.
Chapter 7, Entity Recognition, deals with named entity recognition using R and extends the concepts further to the ontology Learning and expansion concepts.
R 3.3.2 is tested on the following platforms:
Windows® 7.0 (SP1), 8.1, 10, Windows Server® 2008 R2 (SP1) and 2012
Ubuntu 14.04, 16.04
CentOS / Red Hat Enterprise Linux 6.5, 7.1
SUSE Linux Enterprise Server 11
Mavericks (10.9), Yosemite (10.10), El Capitan (10.11), Sierra (10.12)
The hardware specification required for this book is as follows:
Processor: Processor 64-bit processor with x86-compatible architecture (such as AMD64, Intel 64, x86-64, IA-32e, EM64T, or x64 chips). ARM chips, Itanium-architecture chips (also known as IA-64), and non-Intel Macs are not supported. Multiple-core chips are recommended.
Free disk space. 250 MB.
RAM. 1 GB required, 4 GB recommended.
If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytic with R, then this book is for you. Experience of working with statistical methods and language processing would be helpful.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, path names, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the include directive."
A block of code is set as follows:
library(prob) S <- rolldie(2, makespace = TRUE) A <- subset(S, X1 + X2 >= 8) B <- subset(S, X1 == 3) #Given Prob(A, given = B)
Any command-line input or output is written as follows:
docs[[1]]$content
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Here is the step where you have to select Advanced system settings."
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