Natural Language Processing

Abstract

In recent years, text mining has become a branch of Natural Language Processing. This chapter focuses on the analysis of textual data, illustrated by examples treated with the software R, which will be used to manipulate texts and strings, to transform them by word embedding methods and to analyse them by supervised and unsupervised statistical and machine learning methods. It discusses methods of topic modeling and sentiment analysis. There are two types of methods in textual data analysis: descriptive methods and predictive methods. The chapter describes the various lexical analyses that are often performed to categorize documents or to detect the topics contained in a set of documents. The analysis of textual data is most often done on vector representations of the data, which are stored in matrices containing the documents in rows and the terms in columns. A recent application of natural language processing is sentiment analysis.