Estimating aggressiveness of russian texts by means of machine learning

Abstract

This paper considers emotional assessment of texts in Russian using machine learning on the example of aggression detection. It summarizes the related work, methods, models and datasets, describes actual problems, proposes a text processing pipeline and a software system for training neural networks on heterogeneous datasets. The experiments show that neural networks trained on the annotated corpora both in Russian and English, allow to determine whether a text item in Russian contains an aggressive message. Authors thoroughly compare different assessment methods, particularly corpus-based approaches, machine learning solutions and hybrid variants. Results, obtained here, can be used to estimate the aggressiveness probability, for example, to rank messages for subsequent manual verification. They also enable feasibility studies on the possibilities of detecting a particular type of emotion in a text using corpora in other languages. The paper highlights further research directions, where different Python toolkits (NLTK, Keras) could be used for better model performance.