Анализ данных в социальных сетях/глубокий анализ текста



Dr. sc. ing.
Дмитрий Павлюк
Факультет инжeнерных наук
Дмитрий Павлюк

Academic degree: doctoral degree in Economics (2005) and Engineering (2015).

Current position at TSI: professor (since 2019), pro-dean of the Engineering Faculty (since 2021), researcher and head of Data Analytics and Artificial Intelligence research cluster at Transport and Telecommunication Institute, TSI (since 2020).

Experience: academic career from an assistant position at Saratov State University, Russia, in 2002 to an associate professor at TSI. Chair of TSI Mathematical Methods and Modelling Department (2014-2020).

Academic experience: Nineteen years of teaching experience in higher education, development and presenting study courses: Probability Theory and Mathematical Statistics; Econometrics; Operation Research; Optimisation Theory; Discrete Mathematics; Introduction to Stochastic Processes; Data Analysis and Business Forecasting. Member of TSI final attestation commissions on MSc in Computer Science and Management of Information Systems. Dr Dmitry Pavlyuk has supervised 15 successfully promoted MSc and 10 BSc theses. Teaching mobility to Higher School of Transport, Bulgaria (2013), Vilnius Gediminas Technical University, Lithuania (2017).

Research experience: Author of more than 50 publications, including 22 journal articles in Sensors (ISSN: 1424-8220), European Transport Research Review (ISSN: 1867-0717), Algorithms (ISSN: 1999-4893), Transport and Telecommunication (ISSN: 1407-6179), Research in Transportation Economics (ISSN: 0739-8859), Transport (ISSN: 1648-4142) and 12 book chapters in Springer’s Lecture Notes in Networks and Advances in Intelligent Systems and Computing book series. Postdoctoral researcher at TSI (2017-2020) for spatiotemporal urban traffic modelling; presenter at more than 30 international conferences in Latvia, Austria, Cyprus, France, Greece, Germany, Poland, Russia, Spain. The reviewer at many top scientific journals, including Transportation Research, Parts A, C, E, Transport Reviews, IEEE Access, Transportmetrica B. Guest editor at Information (ISSN: 2078-2489); member of programme committees of Reliability and Statistics in Transportation and Communication (RelStat-2017-2020), Latvia, and Computer Modelling in Decision Making (CMDM 2017-2020), Russia. Member of PhD promotion committee, expert of the Latvian Council of Science (since 2017) in Civil Engineering.

Participation in research projects: Dr Pavlyuk has successfully completed the postdoc research project “Spatiotemporal urban traffic modelling using big data” (2017-2020); researcher in “Enhancing excellence and innovation capacity in sustainable transport interchanges” (Horizon 2020 Nr. 692426, 2016-2018), “Learning with ICT use” (Erasmus+, 2014-2017) and several commercial research projects.

Research Interests: Research interests include: spatial and spatiotemporal statistical modelling, multivariate time series analysis, econometric models and statistical estimators, stochastic frontier models and machine learning in applied domains of banking, airport industry, urban road traffic, public transport, venture markets, and environmental management.

Тип проекта: прикладной


Социальные данные производят огромное количество данных, но поиск и обнаружение ценной информации из этих данных сложны. Мы занимаемся сбором и анализом данных социальных сетей (например, твиттер, фейсбук API) для решения прикладных задач. Глубинный анализ текста – основной методологический подход.

Финансирование: внутреннее

Сроки: текущий проект

Инструменты анализа данных и искусственного интеллекта:

  • Глубинный анализ текста
  • Искусственные нейронные сети
  • Алгоритм «случайного леса»


  • Pavlyuk, D., Karatsoli, M., Nathanail, E., 2019. Exploring the Potential of Social Media Content for Detecting Transport-Related Activities, in: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (Eds.), Reliability and Statistics in Transportation and Communication. Springer International Publishing, Cham, pp. 138–149. https://doi.org/10.1007/978-3-030-12450-2_13

Студенческие работы:

  • Jancev, K., 2017. Study of text message clustering by sentiment and semantics. MSc thesis.
  • Girtciuss J., in progress, MSc thesis.
  • Tarhanovs S., in progress, MSc thesis.

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