Sarcasm Relation to Time: Sarcasm Detection with Temporal Features and Deep Learning

Authors

Md Saifullah Razali, Universiti Putra Malaysia
Alfian Abdul Halin, Universiti Putra Malaysia
Yang Wai Chow, University of Wollongong
Noris Mohd Norowi, Universiti Putra Malaysia
Shyamala Doraisamy, Universiti Putra Malaysia

Publication Name

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

This paper presents a deep learning-based framework to detect sarcasm in relation to time. Deep N-gram features generated using the FastText algorithm, combined with temporal handcrafted temporal features are used to train several machine learning classifiers. Experimental results show that Logistic Regression performs the best among all the classifiers. The introduction of the handcrafted temporal features has also whosn to improve overall detection performance when compared to existing works in the field.

Open Access Status

This publication may be available as open access

Volume

14326 LNAI

First Page

287

Last Page

297