Using Transformers for Emotion Recognition in Bangla Text: A Comparative Study of MultiBERT and BanglaBERT with Data Augmentation

Emotion recognition is an exacting task due to the complexity and diversity of human emotions, as well as their tendency to be open to multiple interpretations. Despite these challenges, emotion recognition from textual data has achieved promising results in recent years for the English language. Numerous studies have been conducted on this topic, leading to significant advancements in various fields, including business, politics, education, research, and healthcare. However, accurate emotion recognition from Bengali (Bangla) text remains difficult due to limited resources available for the Bangla language. Additionally, the available datasets for Bengali text are often poorly annotated or imbalanced, which significantly hampers the performance of text classifiers and causes overfitting. Data augmentation can help address this issue. By addressing class imbalance through data augmentation via back-translation, we analyzed and evaluated the impact of augmentation on both real and synthetic datasets. We used transformer-based models, Multi-BERT and BanglaBERT. Data augmentation considerably boosts the accuracy by 8% and 6% for MultiBERT and BanglaBERT respectively, along with a 13% increase in F1-score for both the models. An ablation study also evaluated the effects of hyperparameters on our models.