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57366-01 - Kolloquium: Text Data in Business and Economics (3 KP)
| Semester | Herbstsemester 2025 |
| Angebotsmuster | unregelmässig |
| Dozierende | Benjamin Arold (benjamin.arold@unibas.ch, BeurteilerIn) |
| Inhalt | 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 |
| Lernziele | 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. |
| Literatur | 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. |
| Anmeldung zur Lehrveranstaltung | Registration: Please enroll in the Online Services (services.unibas.ch); PhD - students of other Swiss Universities first have to register at the University of Basel BEFORE the start of the course 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 registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! If you have any questions, please do not hesitate to contact the Graduate School administration at gsbe-wwz@unibas.ch. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| täglich | Siehe Einzeltermine | ||
| Bemerkungen |
The lecturers are: Dr. Benjamin Arold (ETH Zürich), Dr. Claudia Marangon (ETH Zürich) |
Einzeltermine
| Datum | Zeit | Raum |
|---|---|---|
| Montag 15.12.2025 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Dienstag 16.12.2025 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Mittwoch 17.12.2025 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Donnerstag 18.12.2025 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Freitag 19.12.2025 | 10.15-14.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Module |
Modul: Fachlich-methodische Ausbildung (Promotionsfach: Staatswissenschaften) Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät (Studienbeginn vor 01.02.2024)) |
| Prüfung | Leistungsnachweis |
| Hinweise zur Prüfung | Take-home assignment. |
| An-/Abmeldung zur Prüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,1 |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |