Extracting Contextonyms from Twitter for Stance Detection

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

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.

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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