  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. We hypothesise that estimating dataset complexity allows for the
reduction of the number of required experiments iterations. This way we can
optimize the resource-intensive training of ML models which is becoming a
serious issue due to the increases in available dataset sizes and the ever
rising popularity of models based on Deep Neural Networks (DNN). The problem of
constantly increasing needs for more powerful computational resources is also
affecting the environment due to alarmingly-growing amount of CO2 emissions
caused by training of large-scale ML models. The research was conducted on
multiple datasets, including popular datasets, such as Yelp business review
dataset used for training typical sentiment analysis models, as well as more
recent datasets trying to tackle the problem of cyberbullying, which, being a
serious social problem, is also a much more sophisticated problem form the
point of view of linguistic representation. We use cyberbullying datasets
collected for multiple languages, namely English, Japanese and Polish. The
difference in linguistic complexity of datasets allows us to additionally
discuss the efficacy of linguistically-backed word preprocessing.
