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Towards Improved Deep Contextual Embedding for the identification of Irony and Sarcasm
conference contributionposted on 2020-07-01, 00:00 authored by U Naseem, Imran RazzakImran Razzak, Peter EklundPeter Eklund, K Musial
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.
EventNeural Networks. International Joint Conference (2020 : Online from Glasfow, Scot.)
Pagination1 - 7
LocationOnline from Glasgow, Scotland
Place of publicationPiscataway, N.J.
NotesThe 2020 International Joint Conference on Neural Networks (IJCNN) was held virtually, as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020
Publication classificationE1 Full written paper - refereed
Title of proceedingsIJCNN 2020 : Proceedings of the 2020 International Joint conference on Neural Networks
CategoriesNo categories selected