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75901-01 - Colloquium: Text Analysis in R: From Basic Techniques to Modern AI (3 CP)
| Semester | spring semester 2025 |
| Course frequency | Every spring sem. |
| Lecturers | Dominik Meier (dominik.meier@unibas.ch, Assessor) |
| Content | In this course, students learn the theoretical and practical basics of text analysis with R, from basic data preparation methods to advanced models for Natural Language Processing (NLP). Starting with simple frequency analysis and topic models, we gradually work our way up to more sophisticated methods that include the use of current Large Language Models (LLMs). Various packages, algorithms and evaluation strategies will be introduced and applied in practical exercises. At the end of the course, participants will be able to independently process complex text datasets, develop customized models and critically evaluate modern AI-supported analysis tools. |
| Learning objectives | By the end of this course, students will: 1. Understand the theoretical underpinnings of text analysis in social sciences. 2. Develop and preprocess text datasets for analysis. 3. Apply machine learning methods for text classification and clustering. 4. Implement advanced NLP models, including LLMs and transformers, for diverse tasks. 5. Critically evaluate the use and limitations of AI in text analysis. |
| Bibliography | Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. |
| Course application | The number of participants is limited to 30 per course. Interested students should email Dominik Meier (dominik.meier(at)unibas.ch) before February 10, 2025. Please include: your name, study major, number of completed semesters, and matriculation number, We will select and confirm 30 participants on February 14, 2025. After receiving the confirmation email, you can enroll through the Online Services (services.unibas.ch). Eucor-Students and mobility students of other Swiss Universities or the FHNW first have to register at the University of Basel BEFORE the application deadline and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html. After successful application, you can enroll for the course in the Online Services (services.unibas.ch). |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 12.15-14.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
Dates
| Modules |
General Electives in Business and Economics: Additional Courses (Master's Studies: Business and Economics) Module: Technology Field (Master's Studies: Business and Technology) |
| Assessment format | record of achievement |
| Assessment details | - Participation (10%): Active participation in class discussions, practical exercises, and group activities is essential for fostering a collaborative learning environment. This component will be evaluated based on attendance, engagement in discussions, and contributions to group tasks during hands-on sessions. - Assignments (30%): Students will complete assignments focused on implementing the text analysis methods in R. These assignments are designed to reinforce the concepts and techniques introduced in class and include tasks such as corpus construction, feature extraction, topic modeling, and supervised classification. Assignments will be graded on accuracy, completeness, and clarity of code and documentation. - Final Project (60%): Students will independently conduct a comprehensive text analysis project, applying the methods and tools covered in the course to a dataset of their choice. The project should include: - Problem Definition: A clear research question or practical problem related to text analysis. - Data Preparation: Development and preprocessing of a text dataset. - Methodology: Implementation of relevant text analysis techniques, including at least one advanced method (e.g., LLMs, transformers). - Results and Insights: Presentation of findings with appropriate visualizations and interpretations. - Critical Evaluation: Discussion of limitations, ethical considerations, and potential improvements. The project will culminate in a written report and a final in-class presentation in the last week. Evaluation will be based on originality, methodological rigor, use of course concepts, clarity of presentation, and the depth of critical analysis. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |