Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Digital Transformation and Global Society (DTGS); ITMO Univ, St Petersburg, RUSSIA; ITMO Univ, St Petersburg, RUSSIA

Год издания: 2022

Идентификатор DOI: 10.1007/978-3-030-93715-7_7

Ключевые слова: Classification; Emotion Analysis; Lovheim Cube; Non-discrete emotion; Regression

Аннотация: Nowadays sentiment and emotion analyses are widespread methodologies. However, most of all related tasks in classification manner use discrete classes as target variables: Positive vs Negative (sometimes accompanied by Neutral class), or discrete emotion classes (as Anger, Joy, Fear, etc.). Nonetheless, it is more likely that emotion is not discrete. In this paper, we argue that regression is more natural way to evaluate and predict emotions in text and apply regression framework in study of using Lovheim Cube emotional model for emotion analysis. A regression approach for predicting a point in 3-d space or a configuration of its diagonals can provide us with detailed analytics from an emotional diversity perspective. The preliminary results on regression values prediction performed by five different models demonstrate the need of optimization in regard to a precision. The additional conclusion is that the accuracy of classification is not affected significantly by the target variable type.

Журнал: DIGITAL TRANSFORMATION AND GLOBAL SOCIETY, DTGS 2021

Выпуск журнала: Vol. 1503

Номера страниц: 97-107

ISSN журнала: 18650929

Место издания: CHAM

Издатель: SPRINGER INTERNATIONAL PUBLISHING AG

• Kolmogorova Anastasia (Siberian Fed Univ, 82a Svobodny Ave, Krasnoyarsk 660041, Russia)
• Kalinin Alexander (Siberian Fed Univ, 82a Svobodny Ave, Krasnoyarsk 660041, Russia)
• Malikova Alina (Siberian Fed Univ, 82a Svobodny Ave, Krasnoyarsk 660041, Russia)

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