CLARK at SemEval-2019 task 3: Exploring the role of context to identify emotion in a short conversation

Abstract

With text lacking valuable information available in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naïve Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while training the model significantly decreases performance. Our approach has the additional benefit that its performance rivals a baseline LSTM model while requiring fewer resources.