Memorias de investigación
Communications at congresses:
Semi-automatic training set construction for supervised sentiment analysis in political contexts
Year:2018

Research Areas
  • Physics chemical and mathematical

Information
Abstract
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.
International
Si
Congress
The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018) [http://asonam.cpsc.ucalgary.ca/2018/]
960
Place
Barcelona (Spain)
Reviewers
Si
ISBN/ISSN
978-1-5386-6051-5
10.1109/ASONAM.2018.8508386
Start Date
28/08/2018
End Date
31/08/2018
From page
715
To page
720
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) [https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8488381]
Participants

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Sistemas Complejos
  • Departamento: Ingeniería Agroforestal