Volume 12, Number 3

Sarcasm Detection Beyond using Lexical Features

  Authors

ADEWUYI Joseph Oluwaseyi and OLADEJI Ifeoluwa David, University of Ibadan, Nigeria

  Abstract

In current time, social media websites such as facebook, twitter, and so forth have improved and received substantial importance. These websites have grown into huge environments wherein users explicit their thoughts, perspectives and reviews evidently. Organizations leverage this environment to tap into people’s opinion on their services and to make a quick feedback. This research seeks to keep away from using grammatical words as the only features for sarcasm detection however also the contextual features, which are theories explaining when, how and why sarcasm is expressed. A deep neural network architecture model was employed to carry out this task, which is a bidirectional long short-term memory with conditional random fields (Bi-LSTM-CRF), two stages were employed to classify if a reply or comment to a tweet is sarcastic or non-sarcastic. The performance of the models was evaluated using the following metrics: Accuracy, Precision, Recall, F-measure.

  Keywords

Sarcasm Detection, Deep Learning, Contextual features.