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Memorias de investigación
Ponencias en congresos:
Building training sets for sentiment analysis in Twitter semi-automatically
Año:2019
Áreas de investigación
  • Física química y matemáticas
Datos
Descripción
Standard sentiment analysis techniques usually rely either on sets of rules based on semantic and affective information or in supervised machine learning approaches whose quality heavily depends on the size and significance of a training set of pre-labeled text samples. In many situations, this labeling needs to be performed by hand, potentially limiting the size of the training set. In order to address this issue, in this work we propose a methodology to retrieve text samples from Twitter and automatically label them. We then apply this methodology to a Twitter conversation and assess the quality of the produced training set. Additionally, we also tackle the situation in which the base rates of positive and negative sentiment samples in the training and test sets are biased with respect to the system in which the classifier is intended to be applied. The results presented in this respect hold relevance beyond this particular application.
Internacional
Si
Nombre congreso
NetSci 2019 [https://vermontcomplexsystems.org/events/netsci]
Tipo de participación
970
Lugar del congreso
Burlington (EEUU)
Revisores
Si
ISBN o ISSN
0000-0000
DOI
Fecha inicio congreso
27/05/2019
Fecha fin congreso
31/05/2019
Desde la página
0
Hasta la página
0
Título de las actas
NetSci 2019
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Samuel Martin Gutierrez (UPM)
  • Autor: Juan Carlos Losada Gonzalez (UPM)
  • Autor: Rosa Maria Benito Zafrilla (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Sistemas Complejos
  • Departamento: Ingeniería Agroforestal
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