Исследовательский проект как инструмент обучения методам анализа текста: предсказание класса поста в социальной сети

Алена Владимировна Суворова; Карина Руслановна Смирнова; Евгений Александрович Будин; Татьяна Валентиновна Тулупьева; Александр Львович Тулупьев; Максим Викторович Абрамов

doi:10.32603/2071-2340-3-49-64

Research Project as a Tool for Teaching Text Analysis Methods: Predicting the Post Class in the Social Network

Authors

Алена Владимировна Суворова St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia
Карина Руслановна Смирнова North-West Institute of Management, branch of RANEPA, Saint Petersburg, Russia
Евгений Александрович Будин North-West Institute of Management, branch of RANEPA, Saint Petersburg, Russia
Татьяна Валентиновна Тулупьева St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia
Александр Львович Тулупьев St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia
Максим Викторович Абрамов St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia

DOI:

https://doi.org/10.32603/2071-2340-3-49-64

Keywords:

проблемно-ориентированное обучение, социальные сети, машинное обучение, анализ текста, классификация, автоматизация исследований, язык R

Abstract

The article describes a student research project on predicting the class of a post on a social network based on its textual content. The features of the project are discussed as an integral part of the trajectory of teaching data analysis methods, including text analysis methods and tools that are often not included in machine learning courses. The formulation of the problem, the stages of its solution, the sequence of considering new methods as a way for solving students’ problems, as well as the used tool of the R environment are described. The possibilities of expanding the task and its modifications depending on the level of training of students are given.

Author Biographies

Алена Владимировна Суворова, St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia

Alena V. Suvorova: PhD, Senior Researcher, Theoretical and Interdiciplinary Computer Science Laboratory, SPIIRAS; Associate Professor, HSE University;199178, Russia, St. Petersburg,14-th Line VO, 39, suvalv@gmail.com
Карина Руслановна Смирнова, North-West Institute of Management, branch of RANEPA, Saint Petersburg, Russia

Karina R. Smirnova: student, NWIM RANEPA, Smirnova.KR@mail.ru
Евгений Александрович Будин, North-West Institute of Management, branch of RANEPA, Saint Petersburg, Russia

Evgeniy A. Budin: student, NWIM RANEPA, moyapochta456@gmail.com
Татьяна Валентиновна Тулупьева, St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia

Tatiana V. Tulupyeva: PhD, Associate Professor, Senior Researcher, Theoretical and Interdiciplinary Computer Science Laboratory. SPIIRAS; Associate Professor, NWIM RANEPA; Associate Professor, Computer Science Department, SPSU, tvt100a@mail.ru
Александр Львович Тулупьев, St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia

Alexander L. Tulupyev: PhD, Dc. Sci., Associate Professor, Leading Researcher, Theoretical and Interdiciplinary Computer Science Laboratory, SPIIRAS; Professor, Computer Science Department, SPSU, alt@iias.spb.ru
Максим Викторович Абрамов, St. Petersburg Institute for Informatics and Automation of the RAS, Saint Petersburg, Russia

Maxim V. Abramov: PhD, Researcher, Theoretical and Interdiciplinary Computer Science Laboratory, SPIIRAS; Senior Lecturer, Computer Science Department, SPSU, mva16@list.ru

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Published

2018-06-29

Issue

No. 3 (2018)

Section

Computers in the teaching process

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]

А. В. Суворова, К. Р. Смирнова, Е. А. Будин, Т. В. Тулупьева, А. Л. Тулупьев, and М. В. Абрамов, “Research Project as a Tool for Teaching Text Analysis Methods: Predicting the Post Class in the Social Network”, Компьютерные инструменты в образовании, no. 3, pp. 49–64, Jun. 2018, doi: 10.32603/2071-2340-3-49-64.

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