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A Comparison of Machine Learning Methods of Sentiment Analysis Based on Russian Language Twitter Data

The paper is focused on comparing the performance of different techniques of text tonality analysis, a widely used approach in business to conduct social listening research. However, there are still debates on what type of models perform better for NLP classification tasks. On a corpus of Russian language tweets three models were tested to solve binary classification problem: Logistic regression (LR), XGBoost classifier and Convolutional Neural Network (CNN). The paper descriptively overviews main techniques useful for data cleaning and preprocessing for these methods and covers its possible fitfalls. Based on the study CNN showed best results among chosen models, which goes in line with several articles in the field for other than Russian languages. Together with high predictive results, neural networks exhibit a computational drawback their performance is poor in terms of timings. Besides this, all methods showed high sensitivity to the way data is preprocessed, which is a product of Russian language variability. This leads to a conclusion that there is still a room for improvement in future research more emphasis will be put on hyper parameter tuning for ”boosting type” models and extending list of applied methods.