Authors:
Guillaume Gadek
1
;
Josefin Betsholtz
2
;
Alexandre Pauchet
3
;
Stéphan Brunessaux
2
;
Nicolas Malandain
3
and
Laurent Vercouter
3
Affiliations:
1
Airbus DS, Normandie Univ, INSA Rouen and LITIS, France
;
2
Airbus DS, France
;
3
Normandie Univ, INSA Rouen and LITIS, France
Keyword(s):
Opinion Mining, Context, Contextonyms, Sentiment Analysis, Social Media Data, User Generated Text.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Methods
;
Natural Language Processing
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
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.