The Weighting Factors to Improve Predictability on Twitter

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10045/74530
Información del item - Informació de l'item - Item information
Título: The Weighting Factors to Improve Predictability on Twitter
Autor/es: Arroba Rimassa, Jorge | Muñoz, Rafael | Llopis, Fernando
Grupo/s de investigación o GITE: Procesamiento del Lenguaje y Sistemas de Información (GPLSI)
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Palabras clave: Twitter | Weighting Factors | Formalization of Social Networks
Área/s de conocimiento: Lenguajes y Sistemas Informáticos
Fecha de publicación: 27-feb-2018
Editor: Scientific Research Publishing
Cita bibliográfica: Technology and Investment. 2018, 9(1): 68-79. doi:10.4236/ti.2018.91005
Resumen: The result of the analysis of a thematic in a social network is to find a measure that allows the principal actors to know their performance, that is, they can define or maintain strategies and courses of action in order to optimize their communication. It is necessary to formally define the principles of analysis in Social Networks in order to use their characteristics better and to be able to contextualize the concept and use of weighting factors to improve their predictability. When Social Networks are going to be used as a mechanism to predict social behavior, for example, to predict the outcome of a political election, weighting factors must be introduced to try to match the data collected from the Social Network with those of a sample. In this article we have defined the methodology to incorporate the geographic weighting factors and several formulas have been created that allow reprocessing the data downloaded from Twitter in which its polarity has been determined by classical NLP methods to increase the predictive power.
URI: http://hdl.handle.net/10045/74530
ISSN: 2150-4059 (Print) | 2150-4067 (Online)
DOI: 10.4236/ti.2018.91005
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © 2018 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/
Revisión científica: si
Versión del editor: https://doi.org/10.4236/ti.2018.91005
Aparece en las colecciones:INV - GPLSI - Artículos de Revistas

Archivos en este ítem:
Archivos en este ítem:
Archivo Descripción TamañoFormato 
Thumbnail2018_Arroba_etal_TechInvest.pdf296,81 kBAdobe PDFAbrir Vista previa


Este ítem está licenciado bajo Licencia Creative Commons Creative Commons