The study of what social and political actors say and write can improve our understanding of political conflict and social interactions. To this purpose, scholars have developed several automated content methods to analyse large collections of texts. This note focuses on the structural topic model: a machine learning technique aimed at identifying topics in large-scale text collections with extensions that facilitate the inclusion of document-level metadata. Keywords:
Pinto, L. (2019). Structural Topic Model per le scienze sociali e politiche. POLIS, 33(1), 163-174 [10.1424/92923].
Structural Topic Model per le scienze sociali e politiche
Pinto, Luca
2019
Abstract
The study of what social and political actors say and write can improve our understanding of political conflict and social interactions. To this purpose, scholars have developed several automated content methods to analyse large collections of texts. This note focuses on the structural topic model: a machine learning technique aimed at identifying topics in large-scale text collections with extensions that facilitate the inclusion of document-level metadata. Keywords:File in questo prodotto:
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