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September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.
September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.

 

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Extracting Prominent Aspects of Online Customer Reviews: A Data-Driven Approach to Big Data Analytics

Electronics (MDPI). 2022. Vol. 11. No. 13. Article 2042.
Ali N., Alshahrani A., Alghamdi A., Novikov B.

Sentiment analysis on social media and e-markets has become an emerging trend. Extracting aspect terms for structure-free text is the primary task incorporated in the aspect-based sentiment analysis. This significance relies on the dependency of other tasks on the results it provides, which directly influences the accuracy of the final results of the sentiment analysis. In this work, we propose an aspect term extraction model to identify the prominent aspects. The model is based on clustering the word vectors generated using the pre-trained word embedding model. Dimensionality reduction was employed to improve the quality of word clusters obtained using the K-Means++ clustering algorithm. The proposed model was tested on the real datasets collected from online retailers’ websites and the SemEval-14 dataset. Results show that our model outperforms the baseline models.

Research target: Computer Science
Language: English
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DOI
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Keywords: natural language processingfeature extractionAspect-Based Sentiment AnalysisWord embeddingaspect-term extraction
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