Quantitative Text Analysis Using R

Scraping, Preparing, Visualising and Modelling Data

By: Julian Bernauer, Anna Wohlmann

Write A Review

eText | 14 March 2025 | Edition Number 1

At a Glance

Format
ePUB

eText

$69.94

or 4 interest-free payments of $17.48 with

Instant online reading in your Booktopia eTextbook Library *

Read online on

Desktop

Tablet

Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Grounded in examples from across the social sciences, this book walks you through the process of doing quantitative text analysis step by step. Clear and accessible, it empowers you to progress from beginner level to understanding and using computational social science concepts with ease. Covering key steps in the research process like ethics, data collection, and model choice, it helps you develop important research skills - and equips you with the programming tools you need to handle text data without error.

The textbook offers R software guidance at an easy-to-follow pace, the book presents the coding skills you need to collect and prepare data, providing a strong foundation as you move into data analysis. It will:

Help you develop key data skills like cleaning, managing, classifying and visualizing data
Encourage your ability to be critical and reflective when dealing with data
Offer clear guidance on using messy, real-world data and big data from sources like Wikipedia

Supported by practical online resources including extensive coding examples and software guidance, this book will give you confidence in applying your programming skills and enable you to take control of handling textual data in your own research.