DisasterSense: Event classification in social media using RAKE-based pattern mining and dictionary-driven named entity recognition

Natural disasters have a major effect on the Earth and cause severe repercussions. The use of social networks is a valuable method for gathering information and insight during such situations. Natural language processing techniques that use deep learning and machine learning models show strong potential for categorising disaster-related data. To enhance the classification and recognition of social media information related to natural and man-made disasters, this research introduces a novel approach, DisasterSense . Many existing detection tools misclassify man-made disasters as natural because social media texts often lack clear linguistic patterns. In this study, Dictionary-Driven Named Entity Recognition was applied to create a word-based disaster dictionary for categorising natural and man-made disasters, which was then combined with pattern mining to identify recurrent themes, patterns, and relationships among frequently used phrases. Using frequent patterns extracted with the Rapid Automatic Keyword Extraction (RAKE) model, an unsupervised technique for identifying keywords and phrases from text, a new pattern-based disaster dictionary was developed to enhance classification accuracy through pattern comparison. The effectiveness of the proposed approach was evaluated by comparing it with supervised and unsupervised models. This research aims to help emergency response organisations respond more rapidly and mitigate the impacts of disasters. Experiments on disaster-related data demonstrated that our approach achieved the highest accuracy of 0.789, outperforming all other well-known classification methods evaluated in this study.