  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 by objective experts (psychologists), as the
importance of professional annotation in cyberbullying research has been
indicated multiple times. The study confirmed the effectiveness of Neural
Networks in cyberbullying detection and the correlation between classifier
performance and Feature Density while also proposing a new approach of training
various linguistically-backed embeddings for Convolutional Neural Networks.
