Text Mining and Natural Language Processing

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент социологии

Статус:

Курс обязательный

Когда читается:

2-й курс, 1 модуль

Преподаватель

Ткач Сергей

Course Syllabus

Abstract

For social and political sciences, written text provide essential data for studying ideology and political discourse, conflict, sentiment and political affiliation, among many other things. With a growing availability of larger collections of text in digital form it is tempting to scale the research up in terms of the population studied (e.g. “all of twitter”), time spans (e.g. “all of the American history”), and geographical scope (e.g. “all foreign ties of China”). Computational methods for text analysis promise to aid at the scale where traditional conetnt analysis is not feasible. We will use Python programming environment as a toolbox for text analysis. To “learn by doing” we will work with real text collections and will replicate some methods from the recent social research employing computational text analysis.

Learning Objectives

• The goal of the course is to provide basic understanding on how to properly use collections of texts as quantitative evidence, and to make this knowledge practical.
• Understand the applications of computer text analysis for practical and research tasks.
• Understand the basic stages of processing raw text for subsequent analysis.
• Be able to analyze the tonality and subjectivity of socio -political texts.
• Extract hidden themes from the text.
• Extract structured information from texts.
• Be able to train and work with large language models and classify texts using machine learning methods.

Expected Learning Outcomes

• GPC-1.SOC Capable of reasonably selecting and using modern information and communication technologies to solve professional problems.
• GPC-2.SOC Capable of conducting fundamental and applied sociological research and presenting its results.
• GPC-5.POL Able to develop a strategy for promoting publications in the media based on the basic principles of media management.
• PC-1 Able to use current research results in political science and related disciplines, develop applications of political science to solve practical problems of professional activity
• PC-2 Able to use modern empirical databases (including foreign ones) in scientific and project activities, and independently create databases for the implementation of research and practical tasks.
• PC-3 Able to analyze empirical data (political, economic and sociological research) using modern qualitative and quantitative methods and using appropriate software.
• UC-2 Capable of managing a project at all stages of its life cycle.
• GPC-4.SOC Capable of developing proposals and recommendations for conducting sociological examinations and consulting.
• GPC-3.SOC is capable of predicting social phenomena and processes, identifying socially significant problems and developing solutions based on the use of scientific theories, concepts.

Course Contents

• Towards a textual turn in the social sciences.
• Classical tools of natural language analysis in social sciences.
• Implementation of cognitive mapping and discourse analysis methods.
• Development, presentation and evaluation of the final project.

Assessment Elements

• Сlass participation
  Active involvement in seminars and discussions. Contributes relevant insights and asks questions.
• Final Project
  A comprehensive project combining a presentation (50%) and a written report (50%). Students will present their project, in which they analyze a corpus of texts using the methods covered in the course.
• Final Project Review
  Students write a review of their colleagues' final project . This will assess their ability to critically reflect on the methodology, theoretical validity of the conclusions , and their practical significance.

Interim Assessment

• 2026/2027 1st module
  0.4 * Final Project Review + 0.2 * Сlass participation + 0.4 * Final Project

Bibliography

Recommended Core Bibliography

• Elfrinkhof, A. van, Maks, I., & Kaal, B. (2014). From Text to Political Positions : Text Analysis Across Disciplines. Amsterdam: John Benjamins Publishing Company. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=761345
• Manning, C. D., & Schèutze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=24399

Recommended Additional Bibliography

• Neustein, A. (2014). Text Mining of Web-Based Medical Content. Berlin: De Gruyter. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=887115

Authors

• SNARSKII IAROSLAV ALEKSANDROVICH