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Towards Improved Deep Contextual Embedding for the identification of Irony and Sarcasm
conference contribution
posted on 2020-07-01, 00:00 authored by U Naseem, Imran RazzakImran Razzak, Peter EklundPeter Eklund, K MusialHumans 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.
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Event
Neural Networks. International Joint Conference (2020 : Online from Glasfow, Scot.)Pagination
1 - 7Publisher
IEEELocation
Online from Glasgow, ScotlandPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2020-07-19End date
2020-07-24ISSN
2161-4393eISSN
2161-4407ISBN-13
9781728169262Language
engNotes
The 2020 International Joint Conference on Neural Networks (IJCNN) was held virtually, as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020Publication classification
E1 Full written paper - refereedEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2020 : Proceedings of the 2020 International Joint conference on Neural NetworksUsage metrics
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