  Specific lexical choices in narrative text reflect both the writer's
attitudes towards people in the narrative and influence the audience's
reactions. Prior work has examined descriptions of people in English using
contextual affective analysis, a natural language processing (NLP) technique
that seeks to analyze how people are portrayed along dimensions of power,
agency, and sentiment. Our work presents an extension of this methodology to
multilingual settings, which is enabled by a new corpus that we collect and a
new multilingual model. We additionally show how word connotations differ
across languages and cultures, highlighting the difficulty of generalizing
existing English datasets and methods. We then demonstrate the usefulness of
our method by analyzing Wikipedia biography pages of members of the LGBT
community across three languages: English, Russian, and Spanish. Our results
show systematic differences in how the LGBT community is portrayed across
languages, surfacing cultural differences in narratives and signs of social
biases. Practically, this model can be used to identify Wikipedia articles for
further manual analysis -- articles that might contain content gaps or an
imbalanced representation of particular social groups.
