Textual Data Science with R by Monica Becue-Bertaut

• Main Author: BENOIT, Kenneth
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Models and Methods; Political Science
• Online Access: https://ink.library.smu.edu.sg/soss_research/4014; https://ink.library.smu.edu.sg/context/soss_research/article/5272/viewcontent/Textual_Data_Science_with_R.pdf
• Institution: Singapore Management University

Textual Data Science With R targets an important and rela-tively understudied area of data science: the statistical analysisof largely unstructured data in the form of natural languagetext. Using examples spanning fields such as free-form sur-vey responses, bibliographies, and speeches, the book presentsmulti-dimensional methods for mining patterns and insightsfrom textual data. Beginning with a practical and conceptualoverview of textual data and how to pre-preprocess and struc-ture this data, the book proceeds to explain the framework ofcorrespondence analysis and its application to textual data. Itthen discusses two other major approaches: clustering and afocus on cluster features, including characteristic words, andmultiple factor analysis. It finishes with an extensive practicalsection presenting examples and workflows for bibliographicdatabases, a rhetorical speech, political speeches, and a corpusof sensory descriptions.