# A Robust Deep Ensemble Classifier for Figurative Language Detection

arXiv: https://arxiv.org/abs/2107.04372 · HTML: https://mlsanity.com/paper/2107.04372/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Transformer-based approach to Irony and Sarcasm detection](https://mlsanity.com/paper/1911.10401/) (2019)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News](https://mlsanity.com/paper/1405.1605/) (2014)
- ["Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text](https://mlsanity.com/paper/1709.01950/) (2017)

## Built on by (most cited first, 2 of 2)

- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- ["When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity](https://mlsanity.com/paper/2305.04105/) (2023)

## Abstract (arXiv)

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.