emoji2vec: Learning Emoji Representations from their Description

TL;DR

This paper introduces emoji2vec, a set of pre-trained emoji embeddings derived from their Unicode descriptions, enhancing social media NLP tasks by providing effective emoji representations.

Contribution

The paper presents emoji2vec, the first emoji embeddings learned from Unicode descriptions, enabling improved social media NLP applications without requiring frequent emoji contexts.

Findings

Emoji embeddings outperform skip-gram models in sentiment analysis

Emoji2vec embeddings are readily usable with existing word embeddings

Emoji descriptions effectively capture semantic information

Abstract

Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emoji which are learned from their description in the Unicode emoji standard. The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji need to appear frequently in order to estimate a representation.