Abstract

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 targeted 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.

Highlights

  • Sentiment analysis is a subsection of natural language processing whose objective is to classify a text by the sentiment it contains

  • We comprehensively reviewed the applications of sentiment analysis of the Russian language content and identified current challenges and future research directions

  • We focused on the applications of sentiment analysis of the Russian language content since it was not reviewed before

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Summary

INTRODUCTION

Sentiment analysis is a subsection of natural language processing whose objective is to classify a text by the sentiment it contains. S. Smetanin: Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives. Analysis approaches and their classification performance rather than their applications and obtained outcomes of data analysis In this survey, we comprehensively reviewed the applications of sentiment analysis of the Russian language content and identified current challenges and future research directions. We comprehensively reviewed the applications of sentiment analysis of the Russian language content and identified current challenges and future research directions We considered only those studies, which obtained valuable outcomes based on sentiment analysis of the real-life data, and not considered those, which just trained sentiment classifiers.

BACKGROUND
THE APPLICATIONS OF SENTIMENT ANALYSIS FOR RUSSIAN LANGUAGE TEXTS
CURRENT CHALLENGES
FUTURE RESEARCH OPPORTUNITIES
Findings
CONCLUSION
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