Description
This project aims to use the sentiment pipeline to trace historical shifts in awareness. Many debates - whether about climate change, genetically modified foodstuffs, or #metoo - hint at a high form of awareness in our current global society. It is, however, far less evident where these sentiments are rooted in and how they have evolved over time. We aim to investigate this for the Dutch case by focusing on a genealogical study of central modifiers of awareness - (un)healthy, (not) harmful, etc. We are particularly interested in the roles of multinationals like Shell and Unilever as agents of change in these debates.
To do so, this project develops a historical sentiment analysis pipeline that is based on machine learning. The use of this text mining approach in historical scholarship has been hampered by the manner in which "sentiments" are usually implemented in thesauri and binary systems of sentiment qualifications (positive - negative; based on static lists). With this project we aim to make sentiment analysis more historically dynamic and context-specific.