Geodesy

Quantum mechanics

Irony

Benchmark (surveying)

Linguistics

Voltage

Engineering

Philosophy

Computer science

Visual arts

Literal and figurative language

Sarcasm

Architecture

Natural language processing

Physics

Geography

Autoencoder

Artificial intelligence

Transformer

Artificial neural network

Sentiment analysis

Margin (machine learning)

Metaphor

Convolutional neural network

Machine learning

Electrical engineering

Preprocessor

Neural Computing and Applications

Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164–175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.

A transformer-based approach to irony and sarcasm detection