Publication Type

Book Review

Version

publishedVersion

Publication Date

10-2021

Abstract

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.

Discipline

Models and Methods | Political Science

Research Areas

Political Science

Publication

American Statistician

Volume

75

Issue

4

First Page

453

Last Page

454

ISSN

0003-1305

Identifier

10.1080/00031305.2021.1985864

Publisher

Taylor and Francis Group

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1080/00031305.2021.1985864

Share

COinS