Academic Activities

I received my Ph.D. degree in Feb 2020

Dissertation title: Overcoming degradation in sentiment classification for the collections separated in time.

More than 390 citations, h-index=11, i10-index=11 according to Google Scholar

Research interests
Human-Computer Systems, Natural Language Processing, Knowledge Engineering, Speech Technologies, Computational linguistic, Sentiment analysis, Forecasting, NLP
Awards & Honors
I am ranked among the top 10 NLP researchers in Russia, as reported by the Competence Center of the National Technological Initiative at MIPT in the field of Artificial Intelligence.
Best paper award for Research Design "Reducing the degradation of sentiment analysis for text collection spread over a period of time"

Received the Novosibirsk mayor's grant for young scientists and specialists in the field of innovation activities for the project "
“extraction of data from social networks and sentiment analysis”

The winner of the Prospects of Fundamental Sciences Development conference, “Automatic extraction of terms methods in dynamically updated collections for constructing a dictionary of emotional vocabulary based on the microblogging platform Twitter”.
PC Member
Knowledge Engineering and Semantic Web Conference (KESW 2015–2017),

International Conference on Analysis of Images, Social Networks, and Texts (AIST 2018–2021),

Artificial Intelligence and Natural Language (AINL 2018–2020)
Reviewer and subreviewer
CIKM2022 (ACM International Conference on Information and Knowledge Management)

40th European Conference on Information Retrieval ECIR 2018
2022, University of Bonn. Teaching assistant of Information Retrieval

2021, Novosibirsk State University. Data Science Project management
Selected Publications
Rubtsova, Y. (2018). Reducing the Deterioration of Sentiment Analysis Results Due to the Time Impact. Information, 9(8), 184.
Loukachevitch, N., Blinov, P., Kotelnikov, E., Rubtsova, Y., Ivanov, V., & Tutubalina, E. (2015, May). SentiRuEval: testing object-oriented sentiment analysis systems in Russian. In Proceedings of International Conference Dialog (Vol. 2, pp. 3-13).
Loukachevitch, N. V., & Rubtsova, Y. V. (2016). SentiRuEval-2016: overcoming time gap and data sparsity in tweet sentiment analysis. In Computational Linguistics and Intellectual Technologies (pp. 416-426).
Rubtsova, Y. (2015). Constructing a corpus for sentiment classification training. Softw. Syst, 109, 72-78.
Bondarenko, I., Berezin, S., Pauls, A., Batura, T., Rubtsova, Y., & Tuchinov, B. (2020, November). Using few-shot learning techniques for named entity recognition and relation extraction. In 2020 Science and Artificial Intelligence conference (SAI ence) (pp. 58-65). IEEE.
SentiRuEval competition
Sentiment analysis competition organizer
I co-organized the Russian sentiment analysis evaluation SentiRuEval-2015 and SentiRuEval-2016, which was focused on the reputation monitoring of banks and telecom companies on Twitter. All competition materials can be found on GitHub at this link:
I was responsible for the following:
  • development of a crowdsourcing platform for labeling data;
  • collection and preparation of train and test data;
  • evaluation of participant systems and results;
  • preparing for publication.

The results of labeling were the follows:

  • The labeling was attended by 112 people from 7 countries and 25 cities.
  • In total, 45439 ratings were put down.
  • At least four participants labeled each text. (805 texts - 4 assessors, 7704 - 5 assessors)

Crowdsources statistics
Gender distribution
Age distribution
Education distribution
Bachelor of Science degree in applied mathematics and computer science
In 2003, I enrolled in the Department of Mechanics and Mathematics at the Novosibirsk State University (NSU), located in "science-city" of Akademgorodok in Novosibirsk. NSU is one of the top universities in Russia, offering a combination of basic education and practical experience in the Institutes of the Siberian Branch of the Russian Academy of Sciences. Received a bachelor's degree after successfully defending the thesis "Designing methods and tools for setting queries for research systems using ontology and a subject dictionary."

Master of Science in Mathematics and Computer Science
Execution and presentation of graduate qualification work: “Aspect-based sentiment classification of reviews from twitter for the task of reputational analysis.
With final grade of “Magna cum laude” (excellent).
Ph.D. in engineering, Data Science
In 2020 I defended my Ph.D. thesis. The dissertation title: Overcoming degradation in sentiment classification for the collections separated in time.