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
Citation
BENOIT, Kenneth.(2021). Textual Data Science with R by Monica Becue-Bertaut. American Statistician, 75(4), 453-454.
Available at: https://ink.library.smu.edu.sg/soss_research/4014
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/00031305.2021.1985864