Recognizing Emotions from Texts using a Bert-Based Approach

The popularity of using pre-trained models results from the training ease and superior accuracy achieved in relatively shorter periods. The paper analyses the efficacy of utilizing transformer encoders on the ISEAR dataset for detecting emotions (i.e., anger, disgust, sadness, fear, joy, shame, and guilt). This work proposes a two-stage architecture. The first stage has the Bidirectional Encoder Representations from Transformers (BERT) model, which outputs into the second stage consisting of a Bi-LSTM classifier for predicting their emotion classes accordingly. The results, outperforming that of the state-of-the-art, with a higher weighted average F1 score of 0.73, become the new state-of-the-art in detecting emotions on the ISEAR dataset.