We review several existing text analysis methodologies and explain their formal application processes using the open-source software R and relevant packages. Several text mining applications to analyze central bank texts are presented.

This paper implements a context-based text mining approach in online reviews of three European historic city districts, to assess the data-driven projection of experiences and major determinants of traveler satisfaction. Bivariate analyses, semantic networks of experience concepts, and word...

This Discussion Paper deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO2), one of the most harmful air pollutants. One way to monitor the...

Academia, and economics in particular, faces increased scrutiny because of gender imbalance. This paper studies the job market for entry-level faculty positions. We employ machine learning methods to analyze gendered patterns in the text of 9,000 reference letters written in support of 2,800...

We assess whether online data on vacancies and applications to a job board are a suitable source for studying skills dynamics outside of Europe and the United States, where a rich literature has examined skills dynamics using online vacancy data. Yet, the knowledge on skills dynamics is scarce...

Theory suggests that new market entrants play a special role for the creation of new technological pathways required for the development and diffusion of more sustainable forms of production, consumption, mobility and housing. Unconstrained by past technological investments, entrants can...

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that...