A deep learning-based social media text analysis framework for disaster resource management

Social media has evolved itself as a significant tool used by people for information spread during emergencies like natural or man-made disasters. Real-time analysis of this huge collected data can play a vital role in crisis estimation, response and assistance exercises. We propose a novel prototype system that analyzes the emergency-related tweets to classify them as need or available tweets. The presented system also takes care of non-English tweets as there is no boundary of language for social media users. Several classifiers along with different learning methodologies are used to show their usefulness for an efficient solution. Here, a new supervised learning technique based on word embedding is incorporated in the novel hybrid model that comprises of LSTM and CNN. The system will further give a ranked list of tweets, along with a relevance score for each tweet with respect to the topic. Finally for each of the identified need tweets, its corresponding availability tweets are mapped. For the mapping task, a novel two-word sliding window approach is proposed to generate the combine embedding of two adjacent words. The experimental results show significant improvement in the performance. We evaluate our proposed system with FIRE-2016 and CrisisLex datasets to illustrate its effectiveness during mobilization of needful resources.