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ВИЗУАЛИЗАЦИЯ ДАННЫХ В ЭМОЦИОНАЛЬНОМ АНАЛИЗЕ РУССКОЯЗЫЧНЫХ ИНТЕРНЕТ-ТЕКСТОВ НА ОСНОВЕ МОДЕЛИ "КУБ ЛЁВХЕЙМА"
С. 167–181.
Kolmogorova A., Калинин А. А.
In the paper, we discuss the problem of tools supposed to be effective for visualization of data achieved as result of running algorithms for emotional text analysis. We start by overviewing some technics used to visualize data in projects devoted to exploratory data analysis, sentiment-analysis and emotional text analysis. To continue, we suggest two variants of visualization for emotional representation in texts analyzed within the framework of the theoretical approach known as “Levheim's Cube”, in which emotion is represented as a combination of three neurotransmitters. Such a model makes it possible to operate with spatial coordinates for visualization, but, at the same time, it inevitably imposes a number of restrictions and difficulties.
In book
Издательский дом ЯСК, 2023.
Derkacheva A., Sakirkina M., Kraev G. et al., /. 2026.
Comprehensive data on natural hazards and their consequences are crucial for effective for risk assessment, adaptation planning, and emergency response. However, many countries face challenges with fragmented, inconsistent, and inaccessible data, particularly regarding local-scale events. To address this data gap in Russia, we developed an end-to-end processing pipeline that scrapes news from various online sources, ...
Added: April 28, 2026
Dvoynikova A., Кагиров И. А., Карпов А. А., Информатика и автоматизация (Труды СПИИРАН) 2024
В статье рассматривается проблема распознавания сентимента и эмоций пользователей в русскоязычных текстовых транскрипциях речи с использованием словарных методов и машинного перевода. Количество имеющихся информационных ресурсов для анализа сентимента текстовых сообщений на русском языке очень ограничено, что существенно затрудняет применение базовых методов анализа сентимента, а именно, предобработки текстов, векторизации с помощью тональных словарей, традиционных классификаторов. Для ...
Added: April 25, 2026
Dvoynikova A., В кн.: Сборник трудов IX конгресса молодых ученыхТ. 1.: Университет ИТМО, 2021.
Зачастую коммуникация людей происходит посредством вербального общения. Для распознавания эмоций в речевых высказываниях необходимо анализировать текстовую модальность, т.к. она передает полярность эмоций. В статье описываются способы автоматического распознавания речи, а также предлагается подход сентимент-анализа транскрипции русскоязычной речи на основе тональных словарей. Так как русскоязычные ресурсы до сих пор уступают по объему и качеству англоязычным, приводится ...
Added: April 24, 2026
Dvoynikova A., Верхоляк О. В., Карпов А. А., В кн.: Альманах научных работ молодых ученых Университета ИТМОТ. 3.: Университет ИТМО, 2020.
В работе предлагается метод, основанный на тональных словарях, для анализа тональности разговорной речи. Для проведения экспериментов используются транскрипции аудиозаписей базы данных RAMAS, полученные с помощью систем автоматического распознавания речи. Машинная классификация данных производится на 3 класса: позитивный, нейтральный и негативный с использованием русскоязычных тональных словарей RuSentiLex, LinisCrowd, WordNetAffect и словаря Белякова. ...
Added: April 24, 2026
Dvoynikova A., Карпов А. А., Информационно-управляющие системы 2020 № 4 (107) С. 20–30
Введение: в последние годы анализ тональности, или сентимент-анализ, высказываний пользователей находит практическое применение во многих областях: оценка качества товаров и услуг по отзывам покупателей в Интернете, анализ негативных эмоций в сообщениях, прогноз фондовых рынков, политических ситуаций на основе новостных лент и многих других. В связи с этим разрабатываются разнообразные системы и методы для сентимент-анализа русскоязычных текстовых данных. Цель: выполнение ...
Added: April 24, 2026
Казанцева Г. Н., Dvoynikova A., Наука об искусстве в XXI веке: Материалы V Всероссийской научно-практической конференции молодых ученых 2017 С. 215–222
В статье рассматриваются закономерности позитивного и негативного воздействия на человека звуков и музыки в целом. Раскрываются основные закономерности психоакустики и особенности ее применения в целях музыкальной терапии ...
Added: April 24, 2026
Shadrina E. V., Мохова В. О., Загоскин В. А. et al., Нижегородский психологический альманах 2024 № 2
The article considers the problem of learning of recognizing emotions from pictures. A review and analysis of domestic and foreign works of scientists dealing with the problem of emotional intelligence was carried out. Its formation, influence on human activity and existing variants of its structure were considered, and common features in the understanding of emotional ...
Added: April 9, 2026
Behzadidoost R., Neurocomputing 2025 Vol. 665 P. 1–21
While earlier research has focused on detecting misinformation content, identifying the users who spread it, referred to in this paper as fake information spreaders, remains a relatively new challenge. These users deliberately mix true and false information, making detection more difficult. This paper proposes a textual fingerprint learning model to detect fake information spreaders. The ...
Added: March 12, 2026
Bocharova A., Денисов И. Е., Зуенко И. Ю., Вестник Санкт-Петербургского университета. Востоковедение и африканистика 2025 Т. 17 № 2 С. 366–377
The article focuses on analyzing the perceptions of recent changes in China’s demographic pol-icy by the contemporary Chinese society. These changes involved the relaxation of restrictions on the number of children in a family, first to two children in 2015 and subsequently to three children in 2021. The relevance of this research stems from the ...
Added: February 19, 2026
Faul B., Галактика медиа: журнал медиа исследований 2025 Т. 7 № 4 С. 174 – 197
The article addresses a key challenge to moral justification in normative ethics - the relationship between emotions and retributive moral judgments. Retributivism holds that there are normative reasons to reward or punish individuals on the basis of merit, independent of consequences. A common objection argues that such beliefs are grounded in retributive emotions, which are ...
Added: January 27, 2026
Balakina Y. V., Yin Z., Известия Саратовского университета. Новая серия. Серия: Филология. Журналистика 2025 Vol. 25 No. 2 P. 229–236
The aim of the present study is to trace changes in the construction of the image of China in the British media during two crisis periods: the COVID-19 pandemic and the Russian military operation. Each period encompasses a panic (escalation) phase and a recovery (stagnation) phase. Using data from the Factiva database, 70,356 articles published ...
Added: January 20, 2026
Karpov I., Kirillovich A., Goncharova E. et al., Plos One 2026 Vol. 21 No. 1 Article e0339468
Large language models (LLMs) offer significant potential for constructing commonsense knowledge graphs from text, demonstrating adaptability across diverse domains. However, their effectiveness varies significantly with domain-specific language, highlighting a critical need for specialized benchmarks to assess and optimize knowledge graph construction sub-tasks like named entity recognition, relation extraction, and entity linking. Currently, domain-specific benchmarks are ...
Added: January 15, 2026
Smolyanina E., Morozova I., Харитонова Н. В., Географический вестник 2025 № 4 (75) С. 162–177
The unique tourist experience is one of the main components of tourism activity. However, it is not studied in Russian and Western science. This determined the purpose of the study, that is to identify the characteristics of unique American tourists’ experiences in online reviews about the Lincoln Memorial on the travel site TripAdvisor in the ...
Added: January 7, 2026
Washington, United States of America: AAAI Press, 2025.
AAAI-25 Technical Tracks 23 (Natural Language Processing II) collects peer-reviewed research papers that advance the state of natural language processing, with an emphasis on large language models, efficient inference, instruction following, retrieval augmentation, and multimodal language understanding. The papers address both theoretical and practical challenges, including model efficiency, interactive generation, grounding in external knowledge and ...
Added: December 18, 2025
Kudelya A., Shirnin A., , in: Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025).: Association for Computational Linguistics, 2025. P. 1528–1533.
This paper describes LIBU (LoRA enhanced influence-based unlearning), an algorithm to solve the task of unlearning - removing specific knowledge from a large language model without retraining from scratch and compromising its overall utility (SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models). The algorithm combines classical influence functions to remove the influence of ...
Added: November 17, 2025
Morozov L., Mogilevskii A., Shirnin A., , in: Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025).: Association for Computational Linguistics, 2025. P. 2000–2007.
This paper describes LIBU (LoRA enhanced influence-based unlearning), an algorithm to solve the task of unlearning - removing specific knowledge from a large language model without retraining from scratch and compromising its overall utility (SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models). The algorithm combines classical influence functions to remove the influence of ...
Added: November 17, 2025