Abstract: This article is devoted to the urgent problem of determining the emotional coloring of natural language texts. The article notes that the existing methods for analyzing emotions in text have a number of limitations. The purpose of the article is to develop methods for determining the emotional coloring of texts using partial models. To achieve this goal, the following tasks are solved: creating a specialized data set, training neural networks based on partial models, as well as processing and expanding partial models, building chains of partial models corresponding to sequences of situations for more accurate determination of emotions. The article considers the theory of partial models and its application in the problems of emotional analysis of texts. The concept of an evaluative partial model that formally represents an emotionally colored situation is considered. Particular attention is paid to cases when one situation can evoke several different emotions, possibly opposite in their tonality (ambivalence of emotional assessments). The process of creating a data set with labeled emotional assessments is described. The process of training neural networks based on partial models is considered in detail. The process of analyzing various situations and emotions using trained models is demonstrated. Methods for generating new partial models based on the use of trained neural networks are also described. The advantages of the proposed method are highlighted, including high accuracy of emotion analysis and the possibility of emotional analysis of sequences of situations.

Cite: Akhmedov E.Y. , Palchunov D.E.
Using Partial Models to Extract Emotional Estimations from Natural Language Texts
In compilation Proceedings 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Novosibirsk, Russian Federation, 2024. – IEEE., 2024. – C.282-287. – ISBN 979-8-3315-3202-4. DOI: 10.1109/sibircon63777.2024.10758542 Scopus OpenAlex

Dates:

Published print:	Nov 26, 2024
Published online:	Nov 26, 2024

Identifiers:

Scopus:	2-s2.0-85212136823
OpenAlex:	W4404740434

Citing: Пока нет цитирований

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