  While many different aspects of human experiences have been studied by the
NLP community, none has captured its full richness. We propose a new task to
capture this richness based on an unlikely setting: movie characters. We sought
to capture theme-level similarities between movie characters that were
community-curated into 20,000 themes. By introducing a two-step approach that
balances performance and efficiency, we managed to achieve 9-27\% improvement
over recent paragraph-embedding based methods. Finally, we demonstrate how the
thematic information learnt from movie characters can potentially be used to
understand themes in the experience of people, as indicated on Reddit posts.
