Paper presentation by project coordinator L3s at the Data Science for Social Good!
Authors: Aparup Khatua, Wolfgang Nejdl
Abstract: Machine-driven topic identification of online contents is a prevalent task in the natural language processing (NLP) domain. Social media deliberation reflects society’s opinion, and a structured analysis of these contents allows us to decipher the same. We employ an NLP-based approach for investigating migration-related Twitter discussions. Besides traditional deep learning-based models, we have also considered pre-trained transformer-based models for analyzing our corpus. We have successfully classified multiple strands of public opinion related to European migrants. Finally, we use ‘BertViz’ to visually explore the interpretability of better performing transformer-based models.