Violence-inciting text detection has become critical due to its significance in social media monitoring, online security, and the prevention of violent content. Developing an automatic text classification model for identifying violence in languages with limited resources, like Bangla, poses significant challenges due to the scarcity of resources and complex morphological structures. This work presents a transformer-based method that can classify Bangla texts into three violence classes: direct, passive, and non-violence. We leveraged transformer models, including BanglaBERT, XLM-R, and m-BERT, to develop a hierarchical classification model for the downstream task. In the first step, the BanglaBERT is employed to identify the presence of violence in the text. In the next step, the model classifies stem texts that incite violence as either direct or passive. The developed system scored 72.37 and ranked 14th among the participants.

The amount of online textual content has increased significantly in recent years through social media posts, online chatting, web portals, and other digital platforms due to the significant increase in internet users and their unprompted access via digital devices. Unfortunately, the misappropriation of textual communication via the Internet has led to violence-inciting texts. Despite the availability of various forms of violence-inciting materials, text-based content is often used to carry out violent acts. Thus, developing a system to detect violence-inciting text has become vital. However, creating such a system in a low-resourced language like Bangla becomes challenging. Therefore, a shared task has been arranged to detect violence-inciting text in Bangla. This paper presents a hybrid approach (GAN+Bangla-ELECTRA) to classify violence-inciting text in Bangla into three classes: direct, passive, and non-violence. We investigated a variety of deep learning (CNN, BiLSTM, BiLSTM+Attention), machine learning (LR, DT, MNB, SVM, RF, SGD), transformers (BERT, ELECTRA), and GAN-based models to detect violence inciting text in Bangla. Evaluation results demonstrate that the GAN+Bangla-ELECTRA model gained the highest macro f₁-score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.

Although research on emotion classification has significantly progressed in high-resource languages, it is still infancy for resource-constrained languages like Bengali. However, unavailability of necessary language processing tools and deficiency of benchmark corpora makes the emotion classification task in Bengali more challenging and complicated. This work proposes a transformer-based technique to classify the Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise. A Bengali emotion corpus consists of 6243 texts is developed for the classification task. Experimentation carried out using various machine learning (LR, RF, MNB, SVM), deep neural networks (CNN, BiLSTM, CNN+BiLSTM) and transformer (Bangla-BERT, m-BERT, XLM-R) based approaches. Experimental outcomes indicate that XLM-R outdoes all other techniques by achieving the highest weighted f_1-score of 69.73% on the test data.