An Approach to Analyzing Emotions in Diary Text Entries
A. Filippov, N. Shatalova, N. Yarushkina
2025 International Russian Automation Conference September 7, 2025 DOI: 10.1109/rusautocon65989.2025.11177418 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Population | Russian dream diary texts |
| Key points | The authors propose a hybrid approach combining lexical methods, machine learning, and transformer-based models for emotion classification in Russian dream diary texts. They report 22% precision for classifying two emotions simultaneously and 56% precision for a single emotion, outperforming comparable methods, and an ablation study confirming each component's contribution. |
Abstract
This article introduces a hybrid approach for the classification of emotions in Russian dream diary texts. The research focuses on processing semi-structured data characterized by a high subjectivity in describing real-world entities. In this context, traditional approaches often show limited effectiveness. The article analyzes contemporary techniques for tonality and emotion analysis, highlighting their limitations when applied to the subjective nature of dream content. The proposed approach integrates lexical methods, machine learning algorithms, and transformer-based models to enhance performance. The result of the article is also an annotated dataset, which includes labels for six basic emotions, according to the Paul Ekman classification scheme. Experimental results show that the hybrid model achieves a precision of 22% in classifying two emotions simultaneously. It also achieves a 56% precision in classifying a single emotion. Overall, it outperforms comparable methods. We conducted an ablation study to validate the contribution of each component within the hybrid system, confirming their significance for overall performance.