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:

Structural Topic Model per le scienze sociali e politiche / Pinto, Luca. - In: POLIS. - ISSN 1120-9488. - STAMPA. - 33:1(2019), pp. 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:
2019
Structural Topic Model per le scienze sociali e politiche / Pinto, Luca. - In: POLIS. - ISSN 1120-9488. - STAMPA. - 33:1(2019), pp. 163-174. [10.1424/92923]
Pinto, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/685335
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