AUTOMATIC EMOTION IDENTIFICATION IN RUSSIAN TEXT MESSAGES

Abstract

Automatic emotive text analysis has demonstrated its relevance in recent years. In this paper, we address the issue of identification emotions in the text of informal internet-discourse of the Russian language. We consider text messages collected from Telegram and VK. Due to difficulty of such advanced form of sentiment analysis, this paper proposes an integrated approach to combining linguistic methods and machine learning. As a result, an automatic classifier of text messages on expressed emotions is designed. On testing, our model is estimated to provide near-human performance.