In this comment, I am presenting several issues concerning the paper Obesity of politicians and corruption in post-Soviet countries, written by Pavlo Blavatskyy and published in Economics of Transition and Institutional Change (2020). The issues are grouped into two categories: conceptual, which deals with research design and ethics; and methodological, which includes specific statistical methods, concepts and decisions. While these aspects are important in doing valid and beneficial research, some of them carry inherent subjectiveness. Therefore I posit these arguments not as judgements but expanded (and hopefully substantiated) talking points. 1 I did not ask for the original data and analysis notes, as the paper included an appendix of photos used, BMI-estimation algorithm, and though the other data points were not referenced directly, they were identifiable from the text. How to cite this article: Márk Kis G. Conceptual and methodological issues in Obesity of politicians and corruption in post-Soviet countries-A comment.

BERT is a state-of-the-art open-sourced NLP solution for a wide range of tasks, but even basic uses need extensive GPU resources, technical expertise, and a large and good quality corpus with wide domain coverage. This paper presents an alternative, cost-efficient approach for using the strength of BERT’s tokenizer and contextual embeddings for virtually free, with only nominal Python and Machine Learning expertise. This method of using traditional ML-modeling enhanced by BERT’s contextual embeddings extracted from the hidden layers gives worse but comparable results to fine-tuned full BERT models. On our novel, manually validated dataset of Hungarian online media texts, seven emotions, and a neutral class were classified with a 0.62 weighted F1-score. While two categories were underperforming due to small sample representation and size, the other six produced results that sit squarely between classical frequency-based methods (0.44-0.47) and the newer, fine-tuned BERT models (0.71-0.73). We outline several routes for improvements and conclude that while ’doodling’ around with BERT is a large undertaking for many researchers, our approach provides good results with a fraction of the effort needed. The gap between our approach and a fine-tuned BERT significantly narrows down on corpuses necessitating parameter degradation due to insufficient memory.