  In this paper we present deep-learning models that submitted to the
SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all
subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a
multi-layer self attention mechanism. The attention mechanism improves the
model performance and allows us to identify salient words in tweets, as well as
gain insight into the models making them more interpretable. Our model utilizes
a set of word2vec word embeddings trained on a large collection of 550 million
Twitter messages, augmented by a set of word affective features. Due to the
limited amount of task-specific training data, we opted for a transfer learning
approach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A.
The proposed approach ranked 1st in Subtask E "Multi-Label Emotion
Classification", 2nd in Subtask A "Emotion Intensity Regression" and achieved
competitive results in other subtasks.
