  Although research on emotion classification has significantly progressed in
high-resource languages, it is still infancy for resource-constrained languages
like Bengali. However, unavailability of necessary language processing tools
and deficiency of benchmark corpora makes the emotion classification task in
Bengali more challenging and complicated. This work proposes a
transformer-based technique to classify the Bengali text into one of the six
basic emotions: anger, fear, disgust, sadness, joy, and surprise. A Bengali
emotion corpus consists of 6243 texts is developed for the classification task.
Experimentation carried out using various machine learning (LR, RF, MNB, SVM),
deep neural networks (CNN, BiLSTM, CNN+BiLSTM) and transformer (Bangla-BERT,
m-BERT, XLM-R) based approaches. Experimental outcomes indicate that XLM-R
outdoes all other techniques by achieving the highest weighted $f_1$-score of
$69.73\%$ on the test data. The dataset is publicly available at
https://github.com/omar-sharif03/NAACL-SRW-2021.
