  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.
