Distributed Supervised Sentiment Analysis of TweetsIntegrating Machine Learning and Streaming Analytics for Big Data Challenges in Communication and Audience Research

  1. Arcila Calderón, Carlos 1
  2. Ortega Mohedano, Félix 1
  3. Álvarez, Mateo 2
  4. Vicente Mariño, Miguel 3
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 Universidad Rey Juan Carlos
    info

    Universidad Rey Juan Carlos

    Madrid, España

    ROR https://ror.org/01v5cv687

  3. 3 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

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Revista:
Empiria: Revista de metodología de ciencias sociales

ISSN: 1139-5737

Año de publicación: 2019

Título del ejemplar: La investigación empírica en comunicación

Número: 42

Páginas: 113-136

Tipo: Artículo

DOI: 10.5944/EMPIRIA.42.2019.23254 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Empiria: Revista de metodología de ciencias sociales

Resumen

El análisis a gran escala de tweets en tiempo real utilizando el análisis de sentimiento supervisado representa una oportunidad única para la investigación de comunicación y audiencias. El poner juntos los enfoques de aprendizaje automático y de analítica en tiempo real en un entorno distribuido puede ayudar a los investigadores a obtener datos valiosos de Twitter con el fin de clasificar de forma inmediata mensajes en función de su contexto, sin restricciones de tiempo o almacenamiento, mejorando los diseños transversales, longitudinales y experimentales con nuevas fuentes de datos. A pesar de que los investigadores de comunicación y audiencias ya han comenzado a utilizar los métodos computacionales en sus rutinas, la mayoría desconocen el uso de las tecnologías de computo distribuido para afrontar retos de dimensión big data.  Este artículo describe la implementación de métodos de aprendizaje automático paralelizados en Apache Spark para predecir sentimientos de tweets en tiempo real y explica cómo este proceso puede ser escalado usando computación distribuida tanto comercial como académica, cuando los ordenadores personales son insuficientes para almacenar y analizar los datos. Se discuten las limitaciones de estos métodos y sus implicaciones en los estudios de medios, comunicación y audiencias.

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Fuente de los datos: Dialnet