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 emojis which are learned from their description in the Unicode emoji standard. 1 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 emojis need to appear frequently in order to estimate a representation.
In contrast, REF attempted to learn vector embeddings of emojis based on their short descriptions in the Unicode standard.
emoji2vec: Learning Emoji Representations from their Description
ArXiv
ArXiv
Computer Science