Tweet Emoji Prediction Using Hierarchical Model with Attention.

With the development of social media, a huge number of users are attracted by social platforms such as Twitter. Emojis are widely used by social network users when posting messages. Therefore, it is important to mine the relationships between plain texts and emojis. In this paper, we present a neural approach to predict multiple emojis evoked by plain tweets. Our model contains three modules, i.e., a character encoder to learn representations of words from original characters using convolutional neural network (CNN), a sentence encoder to learn representations of sentences using a combination of long short-term memory (LSTM) network and CNN, a multi-label classification module to predict the emojis evoked by a tweet. Besides, attention mechanism is applied at word-level to select important contexts. Our approach is self-labeling and free from expensive and time-consuming manual annotation. Experiments on real-world datasets show that our model outperforms several automatic baselines as well as humans in this task.