Computational News Analysis: Methods and Applications for Slovene

Abstract

The talk will present natural language processing methods for automated news analysis, with a focus on Slovene. We will start with solutions for typical tasks in the media industry: keyword extraction, named entity recognition, sentiment analysis, and automatic news categorisation. We will then look at deeper questions that interest media and discourse researchers: how language and topics in the news change over time, and how computational methods can reveal framing and bias in news coverage. 

Senja Pollak is a researcher at the Jožef Stefan Institute in Slovenia, where she leads a team working on natural language processing, with a focus on supporting interdisciplinary research in the digital humanities and computational social sciences. She has worked extensively on news analysis: she coordinated the H2020 project EMBEDDIA on cross-lingual news analysis, co-led the ROBACOFI project on comment filtering, led the national project CANDAS on multilingual news discourse analysis, and serves as a work package leader in the LLM4DH project. She is a member of ELLIS (ELLIS Unit Slovenia) and is also a work package leader in the ELLIOT project.