EmoDet2: Emotion Detection in English Textual Dialogue using BERT and BiLSTM Models

Emotion detection is one of the most challenging problems in the automated understand of language. Understanding human emotions using text without facial expression is considered a complicated task. Therefore, building a machine that understands the context of the sentences and differentiates between emotions has motivated the machine learning community recently. We propose a system to detect emotions using deep learning approaches. The main input to the system is a combination of GloVe word embeddings, BERT Embeddings and a set of psycholinguistic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet2) is combining a fully connected neural network architecture and BiLSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.748) over the baseline model provided by Semeval-2019 / Task-3 organizers (F1-score 0.58).