Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model.

ABSTRACT: Sarcasm emerges as a common phenomenon across social networking sites because people express their negative thoughts, hatred and opinions using positive vocabulary which makes it a challenging task to detect sarcasm. Although various studies have investigated the sarcasm detection on baseline datasets, this work is the first to detect sarcasm from a multi-domain dataset that is constructed by combining Twitter and News Headlines datasets. This study proposes a hybrid approach where the convolutional neural networks (CNN) are used for feature extraction while the long short-term memory (LSTM) is trained and tested on those features. For performance analysis, several machine learning algorithms such as random forest, support vector classifier, extra tree classifier and decision tree are used

SUBMITTER: Jamil R 

PROVIDER: S-EPMC8409330 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature