Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
This work addresses emotion analysis for Bangla language users, but it is incremental as it applies existing methods to a new dataset.
The researchers tackled emotion classification in Bangla text by developing a transformer-based model for six emotion classes, achieving high performance on a newly created unified corpus.
In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes: anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labeled datasets of Bangla comments on six emotion classes with fresh manually labeled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.