Untangling Emoji Popularity Through Semantic Embeddings
Emojis have gone viral on the Internet across platforms and devices. Interwoven into our daily communications, they have become a ubiquitous new language. However, little has been done to analyze the usage of emojis at scale and in depth. Why do some emojis become especially popular while others don't? How are people using them among the words? In this work, we take the initiative to study the collective usage and behavior of emojis, and specifically, how emojis interact with their context. We base our analysis on a very large corpus collected from a popular emoji keyboard, which contains a full month of inputs from millions of users. Our analysis is empowered by a state-of-the-art machine learning tool that computes the embeddings of emojis and words in a semantic space. We find that emojis with clear semantic meanings are more likely to be adopted. While entity-related emojis are more likely to be used as alternatives to words, sentiment-related emojis often play a complementary role in a message. Overall, emojis are significantly more prevalent in a sentimental context.