Data-Driven Model for Emotion Detection in Russian Texts

摘要

An important task in the field of automatic data analysis is detecting emotions in texts. The paper presents the approach of emotion recognition for text data in Russian. To conduct an emotion analysis, a method was created based on vector representations of words obtained by the ELMo language model, and subsequent processing by an ensemble classifier. To configure and test the created method, a specially prepared dataset of texts for five basic emotions - joy, sadness, anger, fear, and surprise - is used. The dataset was prepared using a crowdsourcing platform and a home-grown procedure for collecting and controlling annotators’ markup. The overall accuracy is 0.78 (by the F1-macro score), which is currently the new state of the art for Russian. The results can be used for a wide range of tasks, for example: monitoring social moods, generating control signals for mobile robotic systems, etc.