Discovering topics in Twitter about the COVID-19 outbreak in Spain

  1. Marvin M. Agüero Torales
  2. David Vilares Calvo
  3. Antonio G. López Herrera
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Ano de publicación: 2021

Número: 66

Páxinas: 177-190

Tipo: Artigo

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Outras publicacións en: Procesamiento del lenguaje natural

Resumo

En este trabajo, analizamos lo que los usuarios han estado discutiendo en Twitter durante el comienzo de la pandemia causada por el COVID-19. Concretamente, analizamos tres fases diferenciadas de la crisis del COVID-19 en España: el propio tiempo de pre-crisis, el estallido de la enfermedad y el confinamiento. Para llevar esto a cabo, primero recolectamos una gran cantidad de tuits que son preprocesados. A continuación, agrupamos los tuits en distintas temáticas usando un modelo de Latent Dirichlet Allocation, y definimos estrategias generativas y discriminativas para extraer las palabras clave y oraciones más representativas para cada tema. Finalmente, incluimos un exhaustivo análisis cualitativo sobre dichos temas, y cómo estos se corresponden con distintas problemáticas surgidas en España en distintos momentos de la crisis.

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Información de financiamento

MMAT has been partially funded by Barcelona Supercomputing Center (BSC) through the Spanish Plan for advance ment of Language Technologies ‘Plan TL’ and the Secretaría de Estado de Digital-ización e Inteligencia Artificial (SEDIA). DV is supported by MINECO (TIN2017-85160-C2-1-R), by Xunta de Galicia (ED431C 2020/11), by Centro de Investigación de Galicia ‘CITIC’ (European Regional Development Fund-Galicia 2014-2020 Program, ED431G 2019/01), and by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation.

Financiadores

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