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News
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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Where Is Happily Ever After? A Study of Emotions and Locations in Russian Short Stories of 1900–1930

P. 123–135.
Moskvina A., Kirina M.

The paper tackles the problem of the automatic detection of emotions in literary texts using distributional semantics techniques. The experiment was carried out on the material of Russian short stories from the 1900-1930s. We investigated the emotional lexis distribution across different locations in narratives. At first, we calculated the semantic association score between each word in a corpus and a certain emotion label. This stage resulted in twelve ordered lists of semantic associates for each emotion label: fear, anger, sadness, happiness, shame, pride, disgust, pleasure, surprise, relief, amusement, and content. Then, we calculated the frequency of these emotional words per story and normalized the values. Anger turned out to be the most prominent emotion expressed in the texts. In the next stage, we compared obtained distributions with the information on where the action takes place in each story. That data was received from previous annotations by readers. Based on mean relative frequencies, the places with the highest emotionality appeared to be apartment, theater, Petersburg, pub, and hall. Additionally, the emotional diversity of a few thematically specific locations is considered.

Language: English
Full text
DOI
Keywords: sentiment analysisdistributional semanticsRussian short storyemotional lexicon
Publication based on the results of:
Текст как Big Data: моделирование конвергентных процессов в языке и речи цифровыми методами (2023)

In book

Digital Geography: Proceedings of the International Conference on Internet and Modern Society (IMS 2023)
Springer, 2023.
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