Bangla Sentiment Analysis On Highly Imbalanced Data Using Hybrid CNN-LSTM & Bangla BERT

Sentiment analysis is a technique that combines machine learning and natural language processing to identify the emotional attitude of a text. This is a very active research area in recent years. Bengali is the fifth most spoken Indo-European language in the world. Many people in Bangladesh use news portals and social media to gather information on various topics. We used a publicly available dataset from Kaggle. This data set consists of more negative reviews than positive reviews. We try to experiment with this dataset with different models, such as traditional ML models and deep learning models like CNN, LSTM, and the transformer model (Bangla-BERT-base). The Bangla-BERT-base achieved a notable 96% accuracy through 10-fold cross-validation. Several other performance measures are also used to evaluate our model.