44 citations · 94 across the 5 of their papers we have counts for
5 papers
Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui +2
In this research. we analyze the potential of Feature Density (HD) as a way to comparatively estimate machine learning (ML) classifier performance prior to training. The goal of th…
Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui +4
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 specifi…
Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui +3
We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-…
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density
Juuso Eronen, Michal Ptaszynski, Fumito Masui +3
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 use…
Cyberbullying Detection -- Technical Report 2/2018, Department of Computer Science AGH, University of Science and Technology
Michał Ptaszyński, Gniewosz Leliwa, Mateusz Piech +1
The research described in this paper concerns automatic cyberbullying detection in social media. There are two goals to achieve: building a gold standard cyberbullying detection da…