Neural Embeddings for Text Analysis: A Case Study in Neoliberal Discourse
Katerina Mandenaki *
Department of Communication and Media Studies, National and Kapodistrian University of Athens, Athens, Greece.
Catherine Sotirakou
Department of Communication and Media Studies, National and Kapodistrian University of Athens, Athens, Greece.
Constantinos Mourlas
Department of Communication and Media Studies, National and Kapodistrian University of Athens, Athens, Greece.
Spiros Moschonas
Department of Communication and Media Studies, National and Kapodistrian University of Athens, Athens, Greece.
*Author to whom correspondence should be addressed.
Abstract
This paper examines the notions of neoliberalism and the financialization and marketisation of public life by using computational tools such as sentence embeddings on a novel corpus of neoliberal articles. More specifically, we experimented with distributional semantics along with several Natural Language Processing (NLP) techniques and machine learning algorithms in order to extract conceptual dictionaries and “seed” words. Our findings show that sentence embeddings reveal repetitive patterns constructed around the given concepts and highlight the mechanical character of an ideology in its function of providing solutions, policies and constructing stereotypes. This work introduces a novel pipeline for computer-assisted research in discourse analysis and ideology.
Keywords: Automated methods, corpus linguistics, discourse analysis, ideology, information extraction, machine learning, neoliberalism, natural language processing