Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density

no code implementations • 2 Nov 2021 • Juuso Eronen, Michal Ptaszynski, Fumito Masui, Aleksander Smywiński-Pohl, Gniewosz Leliwa, Michal Wroczynski

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.

Sentiment Analysis

Cyberbullying Detection -- Technical Report 2/2018, Department of Computer Science AGH, University of Science and Technology

no code implementations • 2 Aug 2018 • Michał Ptaszyński, Gniewosz Leliwa, Mateusz Piech, Aleksander Smywiński-Pohl

There are two goals to achieve: building a gold standard cyberbullying detection dataset and measuring the performance of the Samurai cyberbullying detection system.

Improving text classification with vectors of reduced precision

1 code implementation • 20 Jun 2017 • Krzysztof Wróbel, Maciej Wielgosz, Marcin Pietroń, Michał Karwatowski, Aleksander Smywiński-Pohl

This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification.

Dimensionality Reduction General Classification +2