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In this paper we present an emotion classifier model submitted to theSemEval-2019 Task 3: EmoContext. The task objective is to classify emotion(i.e. happy, sad, angry) in a 3-turn conversational data set. We formulate thetask as a classification problem and introduce a Gated Recurrent Neural Network(GRU) model with attention layer, which is bootstrapped with contextualinformation and trained with a multigenre corpus. We utilize different wordembeddings to empirically select the most suited one to represent our features.We train the model with a multigenre emotion corpus to leverage using allavailable training sets to bootstrap the results. We achieved overall %56.05f1-score and placed 144.

GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual  Information in Models for Emotion Detection in Sentence-level in a Multigenre  Corpus