Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Semi-automatic training set construction for supervised sentiment analysis in political contexts
Año:2018
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 machine learning approaches whose quality heavily depend 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. 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.
Internacional
Si
Nombre congreso
The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018) [http://asonam.cpsc.ucalgary.ca/2018/]
Tipo de participación
960
Lugar del congreso
Barcelona (Spain)
Revisores
Si
ISBN o ISSN
978-1-5386-6051-5
DOI
10.1109/ASONAM.2018.8508386
Fecha inicio congreso
28/08/2018
Fecha fin congreso
31/08/2018
Desde la página
715
Hasta la página
720
Título de las actas
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) [https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8488381]
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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