Text Mining and Social Network Analysis in Arts and Culture

2022/2023

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Management

Course type:

Elective course

When:

2 year, 2 module

Course Syllabus

Abstract

With the advent of new technologies and forms of communication, the modern world began to be characterized by the creation and accumulation of large volumes of very diverse information. Most of the data is stored in text format (for example, scientific articles, posts in social networks, letters, transcripts of speeches, official documents, etc.) or network format. Computational methods for text analysis promise to aid at the scale where traditional content analysis is not feasible. Social network analysis methods allow us to identify patterns of interaction between agents of different levels (people, communities, organizations, words, etc.) Course attendees will learn how to distinguish typical words and expressions of individuals (for example, the president or the hero of cartoons), conduct topic modelling on a big text data (LDA of > 10k posts), build semantic maps (semantic networks of scientific areas) and etc. This course will be dedicated to the major techniques for mining and analyzing text and network data to discover different patterns. The goal of the course is to provide a basic understanding of the proper usage text and network collections in quantitative analysis. R programming environment will be used as a toolbox for text and network analysis.

Learning Objectives

• To learn text-mining tools

Expected Learning Outcomes

• Able to understand the essence of texts and textual data
• Able to mine big volume of textual data from social networks, reviews, etc.
• Able to conduct topic modelling and sentiment analysis in R
• Able to interpret the results of textual analysis

Course Contents

• Text and textual analysis
• Content analysis
• Sentiment analysis
• Topic modeling

Assessment Elements

• Reports
• Exam

Interim Assessment

• 2022/2023 2nd module
  0.5 * Reports + 0.5 * Exam

Bibliography

Recommended Core Bibliography

• Krippendorff, K. (DE-588)136072429, (DE-576)161833357. (2004). Content analysis : an introduction to its methodology / Klaus Krippendorff. Thousand Oaks, Calif. [u.a.]: Sage. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.110340264

Recommended Additional Bibliography

• Iphofen, R., & Tolich, M. (2018). The SAGE Handbook of Qualitative Research Ethics (Vol. 1st). SAGE Publications Ltd.