№2, 2020
SENTIMENT ANALYSIS: PROBLEMS AND SOLUTIONS
Sentiment analysis or opinion mining is a research area that analyses the thoughts, feelings, emotions, assessments and attitudes of people who study the objects such as products, services, organizations, people, problems, events, topics and their characteristics. The development of sentiment analysis is associated with the emergence of a large amount of digital data generated in social networks, forums, blogs, microblogs, etc. In recent years, sentiment analysis has become the subject of extensive research in the areas, such as natural language processing, data mining, analysis of texts and information retrieval. An analysis of sentiments is widely applied in the fields of marketing, finance, political science, social sciences, medical sciences, etc. This popularity is explained by the fact that opinions are crucial in almost all areas of human activity and are the main factor influencing people's behavior. The inability to easily analyze hundreds of thousands of opinions published on social networks, blogs, forums, and other sources of opinions has necessitated the use of computer linguistics, sentiment analysis, or opinion analysis systems. The article discusses the historical and terminological aspects of sentiment analysis, provides information on information sources and areas of application. The main tasks and levels of sentiment analysis are also studied in this paper. Existing problems and solutions are analyzed (pp.111-123).
Keywords: sentiment analysis, opinion mining, sentiment analysis problems, sentiment classification, sentiment analysis levels, machine learning.
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