The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

Sergey Smetanin
2020 IEEE Access  
Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we
more » ... ed the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts. INDEX TERMS Classification, machine learning, computational linguistics, sentiment analysis, applications of sentiment analysis, Russian-language texts, public opinion. SERGEY SMETANIN received the B.S. degree in software engineering and the M.S. degree in business informatics from the National Research University Higher School of Economics, Moscow, Russia, in 2016 and 2018, respectively. His research interests include computational linguistics, sentiment analysis, and mobile applications development.
doi:10.1109/access.2020.3002215 fatcat:3ccxys7nevaf3icf2ih5welfjy