  Usage of emoji in social media platforms has seen a rapid increase over the
last few years. Majority of the social media posts are laden with emoji and
users often use more than one emoji in a single social media post to express
their emotions and to emphasize certain words in a message. Utilizing the emoji
co-occurrence can be helpful to understand how emoji are used in social media
posts and their meanings in the context of social media posts. In this paper,
we investigate whether emoji co-occurrences can be used as a feature to learn
emoji embeddings which can be used in many downstream applications such
sentiment analysis and emotion identification in social media text. We utilize
147 million tweets which have emojis in them and build an emoji co-occurrence
network. Then, we train a network embedding model to embed emojis into a low
dimensional vector space. We evaluate our embeddings using sentiment analysis
and emoji similarity experiments, and experimental results show that our
embeddings outperform the current state-of-the-art results for sentiment
analysis tasks.
