Sarcasm Detection using Hybrid Neural Network

Sarcasm Detection has enjoyed great interest from the research community, however the task of predicting sarcasm in a text remains an elusive problem for machines. Past studies mostly make use of twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of label...

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Bibliographic Details
Published in:arXiv.org 2022-10
Main Authors: Misra, Rishabh, Arora, Prahal
Format: Article
Language:English
Subjects:
Datasets
Neural networks
News
Sentences
Websites
Online Access:Get full text
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Summary:Sarcasm Detection has enjoyed great interest from the research community, however the task of predicting sarcasm in a text remains an elusive problem for machines. Past studies mostly make use of twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. To overcome these shortcoming, we introduce a new dataset which contains news headlines from a sarcastic news website and a real news website. Next, we propose a hybrid Neural Network architecture with attention mechanism which provides insights about what actually makes sentences sarcastic. Through experiments, we show that the proposed model improves upon the baseline by ~ 5% in terms of classification accuracy.
ISSN:2331-8422
DOI:10.48550/arxiv.1908.07414