Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

  1. Hendrik Schuff 1
  2. Jeremy Barnes 1
  3. Julian Mohme 1
  4. Sebastian Pado ́ 1
  5. Roman Klinger 1
  1. 1 University of Stuttgart
    info

    University of Stuttgart

    Stuttgart, Alemania

    ROR https://ror.org/04vnq7t77

Libro:
8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2017: Proceedings of the Workshop
  1. Alexandra Balahur (ed. lit.)
  2. Saif M. Mohammad (ed. lit.)
  3. Erik van der Goot (ed. lit.)

Editorial: The Association for Computational Linguistics

ISBN: 978-1-945626-95-1

Año de publicación: 2017

Páginas: 13-23

Congreso: Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (8. 2017. Copenhagen)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

There is a rich variety of data sets for sen- timent analysis (viz., polarity and subjec- tivity classification). For the more chal- lenging task of detecting discrete emotions following the definitions of Ekman and Plutchik, however, there are much fewer data sets, and notably no resources for the social media domain. This paper con- tributes to closing this gap by extending the SemEval 2016 stance and sentiment dataset with emotion annotation. We (a) analyse annotation reliability and annotation merg- ing; (b) investigate the relation between emotion annotation and the other annota- tion layers (stance, sentiment); (c) report modelling results as a baseline for future work.

Fuente de los datos: Dialnet