The promise of machine-learning- driven text analysis techniques for historical research: topic modeling and word embedding

Abstract

ABSTRACT Building upon our experience implementing a mixed method study combining historical and topic modeling techniques to explore how institutional voids are resolved and their relationship to formal/informal markets, we describe the promise of Topic Modeling techniques for historical studies. Recent advancements – particularly improvements in artificial intelligence and machine learning techniques – have enabled the use of off-the-shelf AI to analyze and process large quantities of data. These techniques reduce research biases and some of the costs previously associated with computational text analysis techniques (i.e. corpus processing time and computational power). We highlight the usefulness of three text analysis techniques – structural topic modeling (STM), dynamic topic modeling (DTM), and word embeddings – and demonstrate their ability to support the generation of novel interpretations. Finally, we emphasize the continuing importance of the author in every step of the research process, especially for abstracting from AI outputs, evaluating competing explanations, inferring meaning, and building theory.