Researchers in the social sciences and beyond are dealing more and more with massive quantities of text data requiring analysis, from historical letters to the constant stream of content in social media. Traditional texts on statistical analysis have focused on numbers, but this book will provide a practical introduction to the quantitative analysis of textual data. Using up-to-date R methods, this book will take readers through the text analysis process, from text mining and pre-processing the text to final analysis. It includes two major case studies using historical and more contemporary text data to demonstrate the practical applications of these methods. Currently, there is no introductory how-to book on textual data analysis with R that is up-to-date and applicable across the social sciences. Code and a variety of additional resources are available on an accompanying website for the book.

Metavariables and Text Analysis Stratified on Metavariables

Metavariables and Text Analysis Stratified on Metavariables

Metavariables and text analysis stratified on metavariables

5.1 The Significance of Stratification and the Importance of Metavariables

Readers who are data scientists will already understand the importance of stratifying analyses. The analysis of text is not different. It becomes interesting, useful, and insightful when observational units can be classified into meaningful groups and when explanatory variables (also referred to as metavariables or covariates) can be found that help explain some variable of particular interest. In such cases, you can assess whether there are, in fact, differences between groups and whether there exist relationships between the selected variable of interest and other explanatory variables. Using correlation and regression methods, the discovery that such relationships exist in the data, can lead ...

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