Mini-dissertation podcast for Rozina Myoya 

Title: Analysing public transport user sentiment
Explore innovative research on public transport in Sub-Saharan Africa, utilising Twitter data to analyse commuter sentiments. Discover how Multilingual Opinion Mining and NLP can improve user experiences, offering valuable insights for policymakers to enhance urban mobility and sustainability in the region.
Disseration Page https://dsfsi.github.io/blog/Rozina-dissertation/

Podcast: Analysing public transport user sentiment #NotebookLM

We are the Data Science for Social Impact research group at the Computer Science Department, University of Pretoria.

Our general areas of work straddle Data Science for Society as well as Local Language Natural Language Processing. These two strands are complementary. Our work in Data Science and Society has allowed us to have a more nuanced approach to understanding the systematic challenges that face being able to do excellent science with local languages. Through Data Science for Society, we have to understand how when one carries through Data Science research, we situate how the users are part of the process. We find that we need to adjust our research to take care of these challenges and innovate in ways we gather direct data or alternative data.

For us, Data Science for Society means being able to improve approaches/methods or scientific tools for DS while enhancing the ways decision-makers can use the insights that come from these tools. Local Language Natural Language Processing is focused on ways to develop new tools, new data and methodology to improve the state of African languages.

Podcast: Analysing public transport user sentiment #NotebookLM

DSFSI