Identification of sarcasm using word embeddings and hyperparameters tuning

Abstract

Around the world, most of the proposed techniques for the identification of sarcasm either take the utterance in isolation or these methods only perform the categorization of the textual data. Very limited work has been done on how to train or manipulate the various parameters related to textual data so that to improve on the accuracy of the classification method. In this article, we are trying to identify the sarcasm in the textual data using neural networks. We have tried to classify the data using convolutional neural networks (CNN), recurrent neural networks (RNN) and a blend of these techniques to improve accuracy. Our work is not limited to the classification of the sarcastic text, we have also tried to measure the impact of the training data, number of epochs and amount of dropout in the network. The paper also discusses the impact of various embedding on the dataset when converting the same dataset into vectors via different word embeddings. We measured the influence of various parameters on the very large-scale Reddit1 corpus.

Subject Classification:

• 62M45

Keywords:

• Sarcasm
• Irony
• Word Embedding
• Deep Learning
• Convolutional Neural Networks (CNN)
• Long Term Short Memory (LSTM)
• Hyperparameter
• Online Social Networks
• Classification