Quantitative Discourse Analysis at Scale - AI, NLP and the Transformer Revolution

Lachlan O'Neill (), Nandini Anantharama (), Wray Buntine () and Simon Angus ()
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Lachlan O'Neill: SoDa Laboratories, Monash Business School
Nandini Anantharama: SoDa Laboratories, Monash Business School
Wray Buntine: Faculty of Information Technology, Monash University

No 2021-12, SoDa Laboratories Working Paper Series from Monash University, SoDa Laboratories

Abstract: Empirical social science requires structured data. Traditionally, these data have arisen from statistical agencies, surveys, or other controlled settings. But what of language, political speech, and discourse more generally? Can text be data? Until very recently, the journey from text to data has relied on human coding, severely limiting study scope. Here, we introduce natural language processing (NLP), a field of artificial intelligence (AI), and its application to discourse analysis at scale. We introduce AI/NLP’s key terminology, concepts, and techniques, and demonstrate its application to the social sciences. In so doing, we emphasise a major shift in AI/NLP technological capability now underway, due largely to the development of transformer models. Our aim is to provide the quantitative social scientists with both a guide to state-of-the-art AI/NLP in general, and something of a road-map for the transformer revolution now sweeping through the landscape.

Keywords: text as data; artificial intelligence; machine learning; natural language processing; transformer models (search for similar items in EconPapers)
JEL-codes: C45 C52 C55 (search for similar items in EconPapers)
Date: 2021-12
New Economics Papers: this item is included in nep-big and nep-cmp
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