Sarcasm Detection using Hybrid Neural Network
TL
TL;DR
A hybrid Neural Network architecture with attention mechanism which provides insights about what actually makes sentences sarcastic is proposed which improves upon the baseline by ~ 5% in terms of classification accuracy.
Abstract
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.
References9 items
1
Augmenting End-to-End Dialog Systems with Commonsense Knowledge
2
Comparative Study of CNN and RNN for Natural Language Processing
3
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
4
Automatic Sarcasm Detection
5