Methods for Extracting Emotional Assessment from Natural Language Texts based on Semantic Technologies and Deep Learning | Sciact - Система учета научной деятельности ИМ СО РАН

Methods for Extracting Emotional Assessment from Natural Language Texts based on Semantic Technologies and Deep Learning Научная публикация

Журнал

Программная инженерия
ISSN: 2220-3397

Вых. Данные

Год: 2026, Том: 17, Номер: 4, Страницы: 179-190 Страниц : 12 DOI: 10.17587/prin.17.179-190

Ключевые слова

sentiment analysis, emotion recognition, natural language processing, deep learning, ontological model, partial model, atomic diagram

Авторы

Palchunov D.E. ¹ , Mironov V.S. ²

Организации

1	Sobolev Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russian Federation
2	Novosibirsk State University, 630090, Russian Federation

Информация о финансировании (1)

Институт математики им. С.Л. Соболева СО РАН

FWNF-2022-0011

Growing volume of text information in natural language these days makes it necessary to develop some valid emotional content analysis these days. The article suggests some methods of emotion recognition in the situations introduced in the Russian texts samples. Methods of identification and representation of emotion occurrence through atomic diagrams of partial models are also given in the article. Some program units for the Logic Text software system are created to reveal and analyse the emotional evaluation. And to formalise emotionally colored situations we use evaluating partial models. Neural networks are used to recognise emotions. Causal relationships algorithm is based on dividing sentences into predicates and situations with the help of Logic Text softwear system.

Palchunov D.E. , Mironov V.S.
Methods for Extracting Emotional Assessment from Natural Language Texts based on Semantic Technologies and Deep Learning
Программная инженерия. 2026. V.17. N4. P.179-190. DOI: 10.17587/prin.17.179-190 РИНЦ OpenAlex

Поступила в редакцию:	21 окт. 2025 г.
Принята к публикации:	21 нояб. 2025 г.
Опубликована в печати:	7 апр. 2026 г.
Опубликована online:	7 апр. 2026 г.

≡ РИНЦ:	89185768
≡ OpenAlex:	W7152508286