  This paper describes the system submitted by ANA Team for the SemEval-2019
Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual
Emotion Detection (HRLCE) model. It classifies the emotion of an utterance
given its conversational context. The results show that, in this task, our
HRCLE outperforms the most recent state-of-the-art text classification
framework: BERT. We combine the results generated by BERT and HRCLE to achieve
an overall score of 0.7709 which ranked 5th on the final leader board of the
competition among 165 Teams.
