  In this paper, we propose an attention-based classifier that predicts
multiple emotions of a given sentence. Our model imitates human's two-step
procedure of sentence understanding and it can effectively represent and
classify sentences. With emoji-to-meaning preprocessing and extra lexicon
utilization, we further improve the model performance. We train and evaluate
our model with data provided by SemEval-2018 task 1-5, each sentence of which
has several labels among 11 given sentiments. Our model achieves 5-th/1-th rank
in English/Spanish respectively.
