Guzel Rinatovna Eremeeva - Kazan Federal University
Ekaterina Vladimirovna Martynova - Kazan Federal University
Amina Aidarovna Khakimova - Kazan Federal University
Liliya Ernstovna Ilikova - Kazan Federal University

This article is devoted to comparison of various approaches towards sentiment analysis (polarity classification) of the English financial news. The purpose of the work is to check, weather higher quality English texts would have higher classification quality and the links between quality of polarity classification and the architectures of the chosen systems. The approaches, presented on SemEval-۲۰۱۷ competition in the appropriate category of tasks, are taken as the basis. A specially collected dataset is used as the source of English financial news. In the end, a conclusion is made about the relationship between the quality of the source of English financial news and the quality of the news data sentiment analysis, made by the compared systems.

Linguistics, Text, Sentiment Analysis, Polarity Detection