This thesis harnesses recent NLP advancements to create sentiment indices that capture public and investor moods through nearly half a million news articles. The novel sentiment measures align closely with established economic indicators, offering actionable insights for investors, businesses, policymakers, and regulators. With minimal cost and near-continuous updates, the approach outlined in this research provides a timely, data-driven alternative to traditional methods, empowering a more responsive, human-centered economic outlook. By rapidly identifying sentiment trends, these measures support informed, sustainable decisions, helping stakeholders understand and adapt to the emotional and social dynamics that shape markets, and possibly other areas.
Jury
The Jury for the award consists of 10 members and a chairman, representing academia and practice.
This thesis critically explores the European Union’s pursuit of technological advancement for border security, focusing on artificial intelligence (AI) within securitization practices. Using discursive analysis of text and images, it examines how migration is framed as a primary security issue, positioning AI as an essential solution. Analyzing three Horizon 2020 projects (ROBORDER, NESTOR, and TRESSPASS), it scrutinizes the ideological assumptions and values in research discourse. Highlighting tensions between technological neutrality and ethical concerns, it raises potential risks to fundamental rights, especially data privacy, and advocates for informed, ethically aware AI policy within the EU’s border and migration management strategies.
Jury on behalf of academia
Jury on behalf of practice