The limitations of irony detection in Dutch social media
In this paper, we explore the feasibility of irony detection in Dutch social media. To this end, we investigate both transformer models with embedding representations, as well as traditional machine learning classifiers with extensive feature sets. Our feature-based methodology implements a variety of information sources including lexical, semantic, syntactic, sentiment features, as well as two new data-driven features to model common sense. Based on patterns in the syntactic structure of tweets, we aim to model the presence of contrasting sentiments, a phenomenon that is known to be indicativ... Mehr ...
Verfasser: | |
---|---|
Dokumenttyp: | journalarticle |
Erscheinungsdatum: | 2024 |
Schlagwörter: | Languages and Literatures / Irony detection / Sarcasm detection / Implicit sentiment modeling / Computational linguistics / Natural language processing / Machine learning / Neural networks / Language models / Social media |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-29033078 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://biblio.ugent.be/publication/01H68PT2CKPQ6H9KVAY53FBFD4 |