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57366-01 - Colloquium: Text Data in Business and Economics (3 CP)
| Semester | fall semester 2026 |
| Course frequency | Irregular |
| Lecturers | Benjamin Arold (benjamin.arold@unibas.ch, Assessor) |
| Content | Much of human knowledge is stored in unstructured formats, in particular in written text. This course teaches methods to process and analyze text data. The learning goals are to understand the structure and concept text-as-data methods, and to evaluate the use of text-analysis tools in business and economics research. The course will conclude with an overview of non-standard data in business and economics beyond text, in particular audio data and image data. The course covers 10 topics, see below. For most topics, a theoretical lecture will be provided first, followed by a discussion of a recent research paper in economics/NLP. This paper discussion will be conducted as a collaborative seminar, where students will take turns to present and discuss the papers. The 10 topics are ordered as follows: - Overview - Dictionaries - Tokenization & Distance - Unsupervised and Supervised ML with Text - Word Embeddings and Linguistic Parsing - Embedding Sequences with Attention - Generative AI; and Using Transformers for YOUR Research - Image Data in Business & Economics - Audio Data in Business & Economics - Ethical Considerations |
| Learning objectives | The learning goals are to understand the structure and concepts of text-as-data methods, and to evaluate the use of text-analysis tools in economics and business research. |
| Bibliography | Books • Jurafsky and Martin, Speech and Language Processing (3d Ed. 2019). o Available here: https://web.stanford.edu/~jurafsky/slp3/ o The standard theory text on computational linguistics. • Natural Language Processing in Python, Third Edition (“NLTK Book”). o Available at nltk.org/book. o Classic treatments of traditional NLP tools. • Aurelien Geron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019) o O’Reilly Book, should be available with an academic account using ETH email. o A great practical book for machine learning and deep learning in Python, but not NLP-focused. We will use material from Chapters 2-4, 7-11, 13, and 15-17. o The deep learning chapters use Keras + TensorFlow. o Jupyter notebooks: https://github.com/ageron/handson-ml2 • Yoav Goldberg, Neural Network Methods for Natural Language Processing (2017) o ETH Library Online Access (email me if this doesn’t work) o A more advanced theoretical treatment of neural networks with an NLP focus, but already somewhat dated. We will use material from Chapters 1-17 and 19. More readings and material will be announced in the course or on request. |
| Course application | 1. Please register for the course and the subsequent assignment by email to the Graduat School (gsbe-wwz@unibas.ch) no later than 17.09.2026. Mandatory information for registering: first and last name and your matriculation number. Please make sure that you are registered for the fall semester and have paid the semester fees before you register for the course. PhD students of other Swiss Universities have to register at the University of Basel BEFORE the enrolment 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 2. A deregistration is possible until 22.09.26 at the latest by email to belegungstorno-wwz@unibas.ch. 3. Your registration will be entered in your Online Services only after the official deadline of the course registration period, i.e. after October 12, 2026. 4. The following applies to everyone: enrollment = registration for the exam/assignment! In case of non-participation after registration it will be noted as "nicht erschienen" in the transcript. If you have any questions, please do not hesitate to contact the Graduate School administration at gsbe-wwz@unibas.ch. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| täglich | See individual dates | ||
Dates
| Date | Time | Room |
|---|---|---|
| Monday 21.09.2026 | 10.15-14.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Tuesday 22.09.2026 | 16.15-20.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Wednesday 23.09.2026 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 24.09.2026 | 10.15-14.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Friday 25.09.2026 | 10.15-14.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Modules |
Modul: Fachlich-methodische Ausbildung (PhD subject: Business and Economics) Modul: Fachlich-methodische Weiterbildung (Doctoral Studies - Faculty of Business and Economics (start of studies before 01.02.2024)) |
| Assessment format | record of achievement |
| Assessment details | Take-home assignment. |
| Assessment registration/deregistration | Registration/deregistration: teaching staff |
| 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 |