Papers › Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
Md Sakib Ullah Sourav, Huidong Wang, Mohammad Sultan Mahmud, Hua Zheng
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.
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