BDNEWS: Advance Bengali Sensitivity Corpus and Classifiers

The development of natural language processing has been facilitated by introducing numerous high-quality datasets and cutting-edge methodologies. Downstream NLP tasks, including sentiment analysis, are essential for comprehending the context and emotional condition of users. Nonetheless, a comprehensive understanding of the broader aspects of text context remains scarce. To address this gap, we introduce a large-scale dataset BDNEWS that encompasses three dimensions of sensitivity: Worldwide, Countrywise, and Personal which are further categorized into two states-Positive and Negative-resulting in a total of six distinct sentiment categories. We manually curated this dataset from various local Bengali online news portals, comprising 6,989 instances. Additionally, to obtain more effective contextual embeddings, we utilized unsupervised techniques such as FastText and Word2Vec, which were employed to train a bespoke Bidirectional Long Short-Term Memory (BiLSTM) model for precise sensitivity classification. FastText consistently surpasses Word2Vec in all metrics, illustrating its superiority in encapsulating the intricacies of Bengali language semantics. FastText achieves a maximum accuracy of 0.795 together with a recall score of 0.79 and a precision of 0.80. Moreover, its capacity to record sequential dependencies in text considerably improves the sensitivity prediction accuracy. These results give important new perspectives for sensitivity prediction in Bengali language applications and guide the next studies in this field.