The recent explosion in digitized and digital text-media is rapidly changing the evidential basis for the humanities. While the humanities used to be the principal scientific consumers of text-based data, the majority of text analysis is now performed by ‘machines’ outside traditional humanistic domains. Text-Analytics applies automated and data-intensive techniques in order to extract useful knowledge from from large collections of linguistic data. In this PhD course, the participant will acquire experience with two major machine learning paradigms (supervised and unsupervised learning) in order to answer research questions fundamental to the humanities: can we classify texts by genres, periods and status and how do surface structures reveal latent semantic properties. The workshop consists of a series of hands-on tutorials with Python combined with useful explanations and illustrations through use-cases. Programming experience is not a requirement, but participants are should to prepare by installing Python and completing three introductory tutorials available on-line.
TEXT DATA MINING,
DAY 1: Text Classification and Supervised Learning
DAY 2: Thematic Analysis and Unsupervised Learning