Mapping texts : computational text analysis for the social sciences / Dustin S. Stoltz and Marshall A. Taylor.

Format:

Book

Author/Creator:

Stoltz, Dustin S., author.

Taylor, Marshall A., author.

Series:

Computational social science.

Oxford scholarship online.

Computational social science

Oxford scholarship online

Language:

English

Subjects (All):

Text data mining.

Physical Description:

1 online resource (326 pages)

Place of Publication:

New York, NY : Oxford University Press, 2024.

Summary:

'Mapping Texts' provides an introduction to computational text analysis that simultaneously blends conceptual treatments with practical, hands-on examples that walk the reader through how to conduct text analysis projects with real data. The book shows how to conduct text analysis in the R statistical computing environment - a popular programming language in data science.

Contents:

Cover

Advance Praise for Mapping Texts

Mapping Texts: Computational Text Analysis for the Social Sciences

Copyright

Dediaction

Contents

Preface

What You Will Learn

What We Left Out

Acknowledgments

Part I: Bounding Texts

1: Text in Context

What Is Language?

What Is Text?

2: Corpus Building

Texts Are Not People

Balance, Range, and Representativeness

Text Metadata

Authors and Audiences

Time and Location

Domains and Media

Text Data

Languages and Dialects

Genres and Topics

Registers and Styles

Redrawing Boundaries

Part II: Prerequisites

3: Computing Basics

Brass Tacks

Coding Environments

Data Objects, Types, and Structures

Dialects of R

Control Processes: Functions, Loops, and Apply

Installing and Loading Packages

Using Python in R

Data Visualization

Where to from Here

4: Math Basics

The Fundamentals

Comparing Vectors

Dot Product

Euclidean Distance and Cosine Similarity

Correlation

Regression

Comparing Distributions

Central Tendency

Dispersion

Types of Distributions

Our Dear Friend, the Matrix

Matrix Projection

Vector Spaces and Singular Value Decomposition

Graphs and Matrix Projection

A Little Math Goes a Long Way

Part III: Foundations

5: Acquiring Text

Public Text Datasets

Optical Character Recognition

Automated Audio Transcription

Application Programming Interfaces (APIs)

Automated Web Scraping

Legal and Ethical Side of Scraping

Terms of Service

Intellectual Property

Individual and Organizational Privacy

6: From Text to Numbers

Units of Analysis

Tokenizing

Chunking

Document Features

Sparsity

Dedicated DTM Functions

Token Distributions

Zipf's Law and Herdan-heaps' Law

Weighting and Norming

Relative Term Frequency.

Term Frequency/inverse Document Frequency

Summary of Weightings

Term Features

Dimension Reduction

Part IV: Below the Document

7: Wrangling Words

Character Encoding

Markup Characters

Removing and Replacing Characters

"misspelled" Words

Removing Words and Stoplists

Replacing Words

Stemming

Lemmatizing

Lemmatizing in French

Wrangling Workflow

8: Tagging Words

Dictionary Tagging

Named-Entity Recognition

How Named-entity Recognition Works

Named Entities in R

Part-of-Speech and Dependency Parsing

How Pos Tagging Works

Part-of-speech Tagging in R

Dependency Parsing

Part-of-speech Tagging for French

Part V: The Document and Beyond

9: Core Deductive

Discrete Indicators

Weighted Indicators

Frequency-weighted

Term-weghted Dictionaries

Selecting and Building Dictionaries

Pre-built Dictionaries

Building Dictionaries With Supervised Learning

10: Core Inductive

Document Similarity

One-mode Projections

Euclidean Distances and Cosine Similarities

Document Clustering

Hierarchical Clustering

K-means Clustering

Topic Modeling

Topic Modeling With Lsa

Topic Modeling With Lda

11: Extended Inductive

Inference and Topic Models

Predicting Topics With Covariates

Topic Prevalence, Conditional on Covariates

Topic Content, Conditional on Covariates

Word Embeddings: The First Generation

Word Embedding Basics

Weighting the Tcm

Dimension Reduction With Singular Value Decomposition

Word Embeddings: The Next Generation

The Global Approach: Glove

The Neural Network Approach: Cbow, Sngs, and Fasttext

Contextualized Embeddings: Elmo and Bert

Inductive Analysis With Word Embeddings

Semantic Change

Semantic Directions and Semantic Centroids

Word Mover's Distance

12: Extended Deductive.

Supervision and Validation

Representing Objects as Features

Splitting Corpora

Classic Training With Supervision

Logistic Regression

Naive Bayes

Training With Neural Networks

Deductive Analysis With Pretrained Models

Neural Networks With Pretrained Embeddings

Concept Mover's Distance With Pretrained Embeddings

Retrofitting Pretrained Embeddings for Deductive Analysis

Inference With Text Networks

Building Text Networks

Centrality

Degree Centrality

Betweenness Centrality

Network Backbones

Univariate Network Inference

Multivariate Network Inference

13: Project Workflow and Iteration

The Paradox of the Complete Map

Containerize Our Projects

Memoing and Datasheets

Repeating, Replicating, and Simulating the Null

Knowledge Takes a Village

Appendix

References

Index.

Notes:

Also issued in print: 2024.

Includes bibliographical references and index.

Description based on online resource and publisher information; title from PDF title page (viewed on November 16, 2023).

Other Format:

Print version: Stoltz, Dustin S. Mapping Texts

ISBN:

0-19-775691-3

0-19-775689-1

0-19-775690-5

OCLC:

1409615563