pranavm98/bert-multiclass-emotion-recognition

BERT-Based Multi-Class Emotion Recognition

Associating specific emotions to short sequences of texts

We used BERT to classify short texts into one of the seven emotions, as opposed to the typical binary (positive/negative) or ternary (positive/negative/neutral) classes.

Our training and testing dataset are the International Survey on Emotion Antecedents and Reactions (ISEAR), consisting of 1096 records with labeled emotions of seven classes: anger, disgust, fear, joy, sadness, shame, and guilt.

We have achieved an overall accuracy of 69% with a macro F1, Recall, and Precision of 0.69. Moreover, this model indicates a good balance of sensitivity and specificity overall. Also, our results have been pretty close to that best metrics reported for the ISEAR dataset, which is 67%.

Contributors: Aiman Haider, Maobin Guo, Pranav Manjunath, Xinyi Pan

Institution: Duke University