Emojis are very common in social media and understanding their underlying semantics is of great interest from a Natural Language Processing point of view. In this work, we investigate emoji prediction in short text messages using a multi-task pipeline that simultaneously predicts emojis, their categories and sub-categories. The categories are either manually predefined in the unicode standard or automatically obtained by clustering over word embeddings. We show that using this categorical information adds meaningful information, thus improving the performance of emoji prediction task. We systematically analyze the performance of the emoji prediction task by varying the number of training samples and also do a qualitative analysis by using attention weights from the prediction task.