Анализ текста в социальных науках

Бакалавриат 2026/2027

ID 1268039

Статус: Курс по выбору (Политология)

Кто читает: Департамент политики и управления

Где читается: Факультет социальных наук

Когда читается: 4-й курс, 3 модуль

Охват аудитории: для своего кампуса

Язык: английский

Кредиты: 5

Контактные часы: 40

Full Syllabus Ask Question

Abstract

This course is designed for students in political science and related social science disciplines. It introduces how textual materials can be transformed into data for empirical research and trains students to select and evaluate text analysis methods in relation to specific research questions. The course covers the basic logic of textual research, as well as the application of dictionary methods, word embeddings, topic models, text classification, pre-trained language models, and large language models in social science text analysis. It is organized around the sequence of research question, text measurement, method selection, and result validation, with particular attention to the appropriate uses, interpretive boundaries, and research applications of different methods, especially the use of large language models in research and the methodological norms associated with them.

Learning Objectives

This course is a methods course designed to support training in social science research. It is positioned as an introduction to text analysis and its more advanced applications. The course emphasizes research orientation, methodological awareness, and basic hands-on skills, with the aim of helping students build the foundation needed to incorporate text analysis methods into course papers, theses, and future empirical research. This course is intended for advanced undergraduate and graduate students in political science, international relations, sociology, public policy, communication, economics, and other social science disciplines. Students are expected to have basic knowledge of social science research methods, including variables, hypotheses, research design, and introductory statistical analysis. Some experience with Python will be helpful for the practical components of the course, but strong programming skills are not required.

Expected Learning Outcomes

By the end of the course, students should be able to understand the basic logic of text as social science data, grasp the core methodological issues in text-based research, identify the appropriate conditions, strengths, and limitations of different methods, and design a basic text analysis plan around a specific research question
Students should also be able to evaluate existing studies from a methodological perspective and develop a clear understanding of the value, proper use, and boundaries of AI tools in research

Course Contents

Week 1: Text in Social Science Research
Week 2: Dictionary Methods and Word Embeddings
Week 3: Topic Models and Unsupervised Learning
Week 4: Supervised Learning and Text Classification
Week 5: Pre-trained Language Models
Week 6: Large Language Models in Research
Week 7: Research Design and Validation
Week 8: Final Project Presentations and Course Conclusion

Assessment Elements

Class participation and attendance
Short assignment
Final research project

Interim Assessment

2026/2027 3rd module
0.4 * Final research project + 0.3 * Class participation and attendance + 0.3 * Short assignment

Bibliography

Recommended Core Bibliography

9781491962992 - Bengfort, Benjamin; Bilbro, Rebecca; Ojeda, Tony - Applied Text Analysis with Python : Enabling Language-Aware Data Products with Machine Learning - 2018 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1827695 - nlebk - 1827695
Eder, M., Rybicki, J., & Kestemont, M. (2016). Stylometry with R: A Package for Computational Text Analysis. R Journal, 8(1), 107–121. https://doi.org/10.32614/RJ-2016-007
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
From text to political positions : text analysis across disciplines, , 2014
Qualitative text analysis : a guide to methods, practice & using software, Kuckartz, U., 2014
Text analysis in R. (2017). Communication Methods and Measures, 11(4), 245–265. https://doi.org/10.1080/19312458.2017.1387238
Ахренова, Н. А. Искусство анализа текста = The Art of Text Analysis : учебное пособие / Н. А. Ахренова, Л. Н. Лунькова, Ю. С. Чернякова. — Москва : ФЛИНТА, 2024. — 84 с. — ISBN 978-5-9765-5499-3. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/413942 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
Белобородова, А. В. Analytical Reading. Text Analysis and Interpretation : учебное пособие / А. В. Белобородова, М. А. Вертилецкая. — Санкт-Петербург : СПбГУП, 2020. — 159 с. — ISBN 978-5-7621-1069-3. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/215414 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
Гольдман, А. А. Стратегия и тактика анализа текста: The Strategy and Tactic of Text Analysis : учебное пособие / А. А. Гольдман. — 4-е изд., стер. — Москва : ФЛИНТА, 2024. — 184 с. — ISBN 978-5-9765-2046-2. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/398543 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Recommended Additional Bibliography

Mehler, A., & Köhler, R. (2007). Aspects of Automatic Text Analysis. Springer.
Text analysis for the social sciences : methods for drawing statistical inferences from texts and transcripts, , 1997

Authors

BOGACHEV MAXIM IGOREVICH
WANG JINHAI

Course Syllabus