  We report on an exploratory analysis of Emoji Dick, a project that leverages
crowdsourcing to translate Melville's Moby Dick into emoji. This distinctive
use of emoji removes textual context, and leads to a varying translation
quality. In this paper, we use statistical word alignment and part-of-speech
tagging to explore how people use emoji. Despite these simple methods, we
observed differences in token and part-of-speech distributions. Experiments
also suggest that semantics are preserved in the translation, and repetition is
more common in emoji.
