Metaheuristic-assisted deep ensemble technique for identifying sarcasm from social media data Online publication date: Wed, 07-Feb-2024
by Geeta Abakash Sahu; Manoj Hudnurkar
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 26, No. 1, 2024
Abstract: Sarcasm is regarded as the enveloping linguistic factor in online documents that describes the deeply-felt subjective and opinions. This paper intends to introduce a sarcasm detection model that classifies words under sarcastic or non-sarcastic forms. Pre-processing is the initial phase, where the stop word removal and tokenisation are performed. The pre-processed data is then subjected to extracting the features, where, 'information gain, chi-square, mutual information, and symmetrical uncertainty-based features' are extracted. As the curse of dimensionality becomes the greatest crisis, optimal feature selection is carried out. For sarcasm detection, an ensemble classifier such as NN, RF, SVM and optimised DCNN is used, in which the weights of DCNN are optimally selected. For optimal feature selection and optimised DCNN, a hybrid optimisation model termed as Clan Updated Grey Wolf Optimisation (CU-GWO) is proposed. Finally, the effectiveness of the proposed algorithm is compared with extant methods in terms of various measures.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook