— with Daniel Matter (TU Munich)
When: Thursday, June 15th, 2023, 10–11.30am
Where: WI Flexroom (A1 04) + Collocall (hybrid)
Abstract: This workshop offers a comprehensive introduction to Topic Modeling, a computational technique designed to unveil hidden semantic information within text datasets. This session caters to those seeking to explore the potential of text analysis. Participants will gain a thorough understanding of Topic Modeling as a concept and its applications in diverse domains such as social media analysis. Through demonstrations, participants will familiarise themselves with prominent algorithms from Latent Dirichlet Allocation (LDA) to BERT-based topic models, gaining insight into the pros and cons of each approach. By the workshop’s conclusion, participants will have acquired a solid foundation in Topic Modeling.
Daniel Matter is a doctoral researcher at the Professorship of Computational Social Science and Big Data at TU Munich. He has a background in Mathematics, Computer Science and Philosophy. His main areas of interest are social networks, opinion dynamics, and the effects of digitalization on democracy and public discourse with a focus on natural language capabilities of artificial intelligence.
Co-organized with the WI research group “Platform Algorithms and Digital Propaganda”.