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Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection
In this research, we study the change in the performance of machine learning (ML) classifiers when various linguistic preprocessing methods of a dataset were used, with the specific focus on linguistically-backed embeddings in Convolutional Neural Networks (CNN).
Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection
We study the selection of transfer languages for automatic abusive language detection.
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density
We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods in order to estimate dataset complexity, which in turn is used to comparatively estimate the potential performance of machine learning (ML) classifiers prior to any training.
Cyberbullying Detection -- Technical Report 2/2018, Department of Computer Science AGH, University of Science and Technology
There are two goals to achieve: building a gold standard cyberbullying detection dataset and measuring the performance of the Samurai cyberbullying detection system.
