Компьютерные методы анализа текста

Бакалавриат 2026/2027

ID 1179601

Статус: Курс по выбору (Социология и социальная информатика)

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 3-й курс, 1, 2 модуль

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Ткач Сергей

Язык: английский

Кредиты: 5

Контактные часы: 48

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Abstract

For social science research, 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 social media users of a town"), time spans (e.g. "all of the Post-Soviet history"), and geographical scope (e.g. "all educational migration in Russia"). Computational methods for text analysis promise to aid at the scale where traditional content analysis is not feasible. During the course we will cover basic word statistics, various exploratory methods, supervised and unsupervised modeling of text phenomena.

Learning Objectives

To provide basic understanding on how to properly use collections of texts as quantitative evidence, and to make this knowledge practical.

Expected Learning Outcomes

Being able to apply computational methods of text analysis (e.g. analysis of word frequency and co-occurrence, document classification, topic modeling) to collections of texts
Being able to apply word embedding and clustering methods to downstream tasks, such as sentiment analysis, ideological scaling etc.
Being able to adequately interpret and report the results of computational text analysis in research papers
Understanding multidimenional representation of lexical meaning and the role of the dimensionality reduction
Understanding possibilities of the automated text analysis as well as its pitfalls and important caveats about applying statistical tests to language data

Course Contents

Towards a textual turn in the social sciences.
Classification as a method in textual analysis
Sentiment — Sentiment analysis
Implementation of cognitive mapping

Assessment Elements

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.
Class participation
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 2nd module
Class Participation: Activity 0.200 + Final Project : Project * 0.400 + Final Project Review: Writing a review * 0.400

Bibliography

Recommended Core Bibliography

Wardhaugh, R., & Fuller, J. M. (2015). An Introduction to Sociolinguistics (Vol. Seventh edition). [Hoboken, NJ]: Wiley-Blackwell. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=862097

Recommended Additional Bibliography

Bamman, D., Eisenstein, J., & Schnoebelen, T. (2012). Gender identity and lexical variation in social media. https://doi.org/10.1111/josl.12080

Authors

SNARSKII IAROSLAV ALEKSANDROVICH

Course Syllabus