Bangla Emergency Post Classification on Social Media using Transformer Based BERT Models

Text classification is one of the most important tasks in Natural Language Processing. As text data is growing rapidly, it needs more computational power to classify the text in a big dataset. The task is difficult for characteristic-rich languages like Bangla. Having good-quality text data significantly affects the outcome of the model that has been used to classify them. Nowadays, social media can be an important source of information. But there is a huge number of data which are of no use. As the use of social media is increasing day by day, people are posting about events around their surroundings. So, it can be an important propaganda of the media. In this study, various text classification methods were used to classify the texts in Bangla from social media, which can be categorized as emergencies that may need immediate actions from the government, local authority, or law enforcement or even may need international attention. Therefore, 5839 social media posts were collected from Facebook and Twitter, which were written in Bangla along with some mixed English words. Then, after preprocessing, various Machine Learning models, Deep Neural Network models, and Transformer based models were applied to classify them. Among these models, transformer-based XLM-RoBERTa outperformed all the other models with an F1-score of 95.25.