Emojis have gained widespread acceptance, globally and cross-culturally. However, Emoji use may also be nuanced due to differences across cultures, which can play a significant role in shaping emotional life. In this paper, we
a) present a methodology to learn latent emotional components of Emojis,
b) compare Emoji-Emotion associations across cultures, and
c) discuss how they may reflect emotion expression in these platforms.
Specifically, we learn vector space embeddings with more than 100 million posts from China (Sina Weibo) and the United States (Twitter), quantify the association of Emojis with 8 basic emotions, demonstrate correlation between visual cues and emotional valence, and discuss pairwise similarities between emotions. Our proposed Emoji-Emotion visualization pipeline for uncovering latent emotional components can potentially be used for downstream applications such as sentiment analysis and personalized text recommendations.