Neural Embeddings for Text Analysis: A Case Study in Neoliberal Discourse
Katerina Mandenaki, Catherine Sotirakou, Constantinos Mourlas, Spiros Moschonas
Journal of Education, Society and Behavioural Science · pp. 196–204 · Published 26 Nov 2021
10.9734/jesbs/2021/v34i1130379Abstract
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.
Cited by 2
Meng-Fei Xiao, Wei-Xiang Gan, F. Hashim · Hong Kong Financial Bulletin · 2026
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