Improving Irony and Stereotype Spreaders Detection using Data Augmentation and Convolutional Neural Network

Abstract

In this paper we describe a deep learning model based on a Data Augmentation (DA) layer followed by a Convolutional Neural Network (CNN). The proposed model was developed by our team for the Profiling Irony and Stereotype Spreaders (ISSs) task proposed by the PAN 2022 organizers. As a first step, to classify an author as ISS or not (nISS), we developed a DA layer that expands each sample in the dataset provided. Using this augmented dataset we trained the CNN. Then, to submit our predictions, we apply our DA layer on the samples within the unlabeled test set too. Finally we fed our trained CNN with the augmented test set to generate our final predictions. To develop and test our model we used a 5-fold cross validation on the labelled training set. The proposed model reaches a maximum accuracy of 0.92 and an average accuracy of 0.89 over the five folds. Meanwhile, on the provided test set the proposed model reaches an accuracy of 0.9278.