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Computational Methods for Text Analysis

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс по выбору
Когда читается:
3-й курс, 1, 2 модуль

Преподаватель

Course Syllabus

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

Learning Objectives

  • provide basic understanding on how to properly use collections of texts as quantitative evidence, and to make this knowledge practical
Expected Learning Outcomes

Expected Learning Outcomes

  • Understanding possibilities of the automated text analysis as well as its pitfalls and important caveats about applying statistical tests to language data.
  • Understanding multidimenional representation of lexical meaning and the role of the dimensionality reduction.
  • 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.
Course Contents

Course Contents

  • Style — Document classification
  • Content — Topic modeling
  • Sentiment — Sentiment analysis
  • Structure — Entities extraction
Assessment Elements

Assessment Elements

  • non-blocking Сourse participation
    0.3* paper summaries/presentations + 0.2 * in-class participation + 0.2 * homework + 0.3 mid-term test
  • non-blocking Final project
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.3 * Final project + 0.7 * Сourse participation
Bibliography

Bibliography

Recommended Core Bibliography

  • Bamman, D., Eisenstein, J., & Schnoebelen, T. (2014). Gender identity and lexical variation in social media[The resear]. Journal of Sociolinguistics, 18(2), 135–160. https://doi.org/10.1111/josl.12080

Recommended Additional Bibliography

  • Jurafsky, D., Chahuneau, V., Routledge, B. R., & Smith, N. A. (2014). Narrative framing of consumer sentiment in online restaurant reviews. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.18543C32