Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection. (arXiv:2206.01889v1 [cs.CL])

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). Moreover, we study the concept of Feature
Density and confirm its potential to comparatively predict the performance of
ML classifiers, including CNN. The research was conducted on a Formspring
dataset provided in a Kaggle competition on automatic cyberbullying detection.
The dataset was re-annotated …

application arxiv cyberbullying detection embedding feature learning machine machine learning study