GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus

In this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : \textit{EmoContext}. Our approach is a Gated Recurrent Neural Network (GRU) model with attention layer is bootstrapped with contextual information and trained with a multigenre corpus, which is combination of several popular emotional data sets. We utilize different word embeddings to empirically select the most suited embedding to represent our features. Our aim is to build a robust emotion classifier that can generalize emotion detection, which is to learn emotion cues in a noisy training environment. To fulfill this aim we train our model with a multigenre emotion corpus, this way we leverage from having more training set. We achieved overall {\%}56.05 f1-score and placed 144. Given our aim and noisy training environment, the results are anticipated.