Research Area:  Machine Learning
Humans use tonal stress and gestural cues to reveal negative feelings that are expressed ironically using positive or intensified positive words when communicating vocally. However, in textual data, like posts on social media, cues on sentiment valence are absent, thus making it challenging to identify the true meaning of utterances, even for the human reader. For a given post, an intelligent natural language processing system should be able to identify whether a post is ironic/sarcastic or not. Recent work confirms the difficulty of detecting sarcastic/ironic posts. To overcome challenges involved in the identification of sentiment valence, this paper presents the identification of irony and sarcasm in social media posts through transformer-based deep, intelligent contextual embedding - T-DICE - which improves noise within contexts. It solves the language ambiguities such as polysemy, semantics, syntax, and words sentiments by integrating embeddings. T-DICE is then forwarded to attention-based Bidirectional Long Short Term Memory (BiLSTM) to find out the sentiment of a post. We report the classification performance of the proposed network on benchmark datasets for irony & sarcasm. Results demonstrate that our approach outperforms existing state-of-the-art methods.
Author(s) Name:  Usman Naseem; Imran Razzak; Peter Eklund; Katarzyna Musial
Journal name:  2020 International Joint Conference on Neural Networks
Publisher name:  IEEE
Paper Link:   https://ieeexplore.ieee.org/document/9207237