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Usage of emoji in social media platforms has seen a rapid increase over thelast few years. Majority of the social media posts are laden with emoji andusers often use more than one emoji in a single social media post to expresstheir emotions and to emphasize certain words in a message. Utilizing the emojico-occurrence can be helpful to understand how emoji are used in social mediaposts 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 learnemoji embeddings which can be used in many downstream applications suchsentiment analysis and emotion identification in social media text. We utilize147 million tweets which have emojis in them and build an emoji co-occurrencenetwork. Then, we train a network embedding model to embed emojis into a lowdimensional vector space. We evaluate our embeddings using sentiment analysisand emoji similarity experiments, and experimental results show that ourembeddings outperform the current state-of-the-art results for sentimentanalysis tasks.

Learning Emoji Embeddings using Emoji Co-occurrence Network Graph