Extracting Contextonyms from Twitter for Stance Detection

Guillaume Gadek, Josefin Betsholtz, Alexandre Pauchet, Stéphan Brunessaux, Nicolas Malandain, Laurent Vercouter

2017

Abstract

Opinion mining on tweets is a challenge: short texts, implicit topics, inventive spellings and new vocabulary are the rule. We aim at efficiently determining the stance of tweets towards a given target. We propose a method using the concept of contextonyms and contextosets in order to disambiguate implicit content and improve a given stance classifier. Contextonymy is extracted from a word co-occurrence graph, and allows to grasp the sense of a word according to its surrounding words. We evaluate our method on a freely available annotated tweet corpus, used to benchmark stance detection on tweets during SemEval2016.

Paper Citation

in Harvard Style

Gadek G., Betsholtz J., Pauchet A., Brunessaux S., Malandain N. and Vercouter L. (2017). Extracting Contextonyms from Twitter for Stance Detection . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 132-141. DOI: 10.5220/0006190901320141

in Bibtex Style

@conference{icaart17,
author={Guillaume Gadek and Josefin Betsholtz and Alexandre Pauchet and Stéphan Brunessaux and Nicolas Malandain and Laurent Vercouter},
title={Extracting Contextonyms from Twitter for Stance Detection},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={132-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006190901320141},
isbn={978-989-758-220-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Extracting Contextonyms from Twitter for Stance Detection
SN - 978-989-758-220-2
AU - Gadek G.
AU - Betsholtz J.
AU - Pauchet A.
AU - Brunessaux S.
AU - Malandain N.
AU - Vercouter L.
PY - 2017
SP - 132
EP - 141
DO - 10.5220/0006190901320141