A Robust Deep Ensemble Classifier for Figurative Language Detection

TL;DR

This paper proposes a deep ensemble classifier for detecting figurative language in social media text, combining advanced data preprocessing, feature extraction, and multiple deep learning models to improve recognition of sarcasm, irony, and metaphor.

Contribution

It introduces a novel deep ensemble soft classifier that integrates various deep learning techniques and specialized features for improved figurative language detection.

Findings

The DESC model outperforms existing methods on benchmark datasets.

Effective feature extraction enhances model performance.

Deep ensemble approach increases robustness and accuracy.

Abstract

Recognition and classification of Figurative Language (FL) is an open problem of Sentiment Analysis in the broader field of Natural Language Processing (NLP) due to the contradictory meaning contained in phrases with metaphorical content. The problem itself contains three interrelated FL recognition tasks: sarcasm, irony and metaphor which, in the present paper, are dealt with advanced Deep Learning (DL) techniques. First, we introduce a data prepossessing framework towards efficient data representation formats so that to optimize the respective inputs to the DL models. In addition, special features are extracted in order to characterize the syntactic, expressive, emotional and temper content reflected in the respective social media text references. These features aim to capture aspects of the social network user's writing method. Finally, features are fed to a robust, Deep Ensemble Soft Classifier (DESC) which is based on the combination of different DL techniques. Using three different benchmark datasets (one of them containing various FL forms) we conclude that the DESC model achieves a very good performance, worthy of comparison with relevant methodologies and state-of-the-art technologies in the challenging field of FL recognition.