Observatorio de I+D+i UPM

Memorias de investigación
Capítulo de libro:
Semi-Automatic Training Set Construction for Supervised Sentiment Analysis in Polarized Contexts
Año:2019
Áreas de investigación
  • Física química y matemáticas,
  • Humanidades y ciencias sociales
Datos
Descripción
Standard sentiment analysis techniques 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 several Twitter conversations and assess the quality of the produced training sets. 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
DOI
10.1007/978-3-030-33698-1_10
Edición del Libro
Editorial del Libro
Springer, Cham
ISBN
978-3-030-33697-4
Serie
Título del Libro
Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation. Lecture Notes in Social Networks
Desde página
177
Hasta página
197
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: Departamento: Ingeniería Agroforestal
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