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Linguistic Feature Assisted Deep Learning Approach towards Multi-label Classification of Crisis Related Tweets

Micro-blogging sites like Twitter, over the last decade, have evolved into a proactive communication channel during mass convergence and emergency events, especially in crisis stricken scenarios. Extracting multiple levels of information associated with the overwhelming amount of social media data generated during such situations remains a great challenge to disaster-affected communities and professional emergency responders. This valuable data, segregated into different informative categories, can be leveraged by government agencies, humanitarian communities as well as citizens to bring about faster response in areas of necessity. In this paper, we address the above scenario by developing a deep Convolutional Neural Network (CNN) for multi-label classification of crisis related tweets. We augment the deep CNN with several linguistic features extracted from tweets, and investigate their usages in classification. Evaluation on a benchmark dataset shows that our proposed approach attains the state-of-the-art performance.