SURV704: Computer-Based Content Analysis II (Practical Project)

Data Analysis, Data Generating Process

In part 1 of this course, participants have learned how to use standard methods of Natural Language Processing (NLP) to support social science research through automatic content analysis. This second part of the course will contain a practical project as an extension of the first theoretical part. Over the course of this project, the participants will apply some of the techniques covered for answering a research question of their choice. The project will consist of four steps in which guidance is provided by the course instructors. In a first step, the participants will define the research problem and sketch a methodology for solving it that contains some text analysis elements. The following two steps consist of preprocessing and analyzing relevant textual resources. In the final step, the results of the text analysis will be used to provide an answer to the research question.

Course objectives: 

By the end of the course, students will be able to:

  • Integrate text analysis into a research methodology and solve a research question from their field using this methodology
  • Define a methodology for solving a research problem that includes automatic text analysis
  • Selected and apply appropriate methods for preprocessing textual resources relevant for their research question.
  • Selected and apply text mining methods to the preprocessed textual resources and conduct systematic experiments.
  • Interpret the experimental results and draw conclusions concerning their research question.

Grading will be based on:

  • Participation in online meetings (25%)
  • Final Project Report (75%)

Participants need to have attended the following IPSDS courses or have corresponding knowledge:

  • SURV673 Introduction to Python and SQL or necessary knowledge in programming in Python: data types & structures, functions & loops, file I/O
  • SURV736 Web Scraping (recommended)
  • SURV703: Computer-Based Content Analysis Part 1 (Theory)

Course Dates


Spring Semester (January – May)


Fall Semester (September – December)