Year: 2022
Type: conference-paper
Abstract: Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawnout syllable, or a straight looking face.Most of the recent work in sarcasm detection has been carried out on textual data.This paper describes how the problem proposed in Task 6: Intended Sarcasm Detection in English (Abu Arfa et al. 2022) has been solved.Specifically, we participated in Subtask B: a binary multi-label classification task, where it is necessary to determine whether a tweet belongs to an ironic speech category, if any.Several approaches (classic machine learning and deep learning algorithms) were developed.The final submission consisted of a BERT based model and a macro-F1 score of 0.0699 was obtained.
Institution: Universidad de Huelva
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4287891146