# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 1 Introduction  
Social media has been increasingly popular for people to share instant feelings, emotions, opinions, stories, and so on. As a leading social platform, Twitter has gained tremendous popularity since its inception. The latest statistical data show that over 500 million Tweets are sent each day, generating a massive amount of social data that are used by numerous upper-level analytical applications to create additional value. Meanwhile, numerous studies have adopted Twitter data to build natural language processing (NLP) applications such as named entity recognition (NER) [1], relation extraction [2], question and answering (Q&A) [3], sentiment analysis [4], and topic modeling [5]. In addition to the social function, Twitter is also becoming a real-time platform to track events, including accidents, disasters, and emergencies, especially in the era of mobile Internet and 5G communication, where smartphones allow people to post an emergency Tweet instantly online. Timing is the most critical factor in making a rescue plan, and the rise in social media brings a unique opportunity to expedite this process. Due to this convenience, more agencies like disaster relief organizations and news agencies are deploying resources to programmatically monitor Twitter, so that first responders can be dispatched and rescue plans can be made at the earliest time. However, processing social media data and retrieving valuable information for disaster prediction requires a series of operations: (1) perform text classification on each Tweet to predict disasters and emergencies; (2) determine the location of people who need help; (3) calculate the priorities to schedule rescues. Disaster prediction is the first and most important step, because a misclassification may result in a waste of precious resources which could have been dispatched to real needs [6]. However, to automate this process, an accurate and robust classifier is needed to distinguish real disaster Tweets from regular ones. Disaster prediction based on Tweets is challenging, because words indicative of a disaster, such as \"fire\", \"flood\", and \"collapse\", can be used by people metaphorically to describe something else. For example, a Tweet message \"On plus side look at the sky last night it was ABLAZE\" explicitly uses the word \"ABLAZE\" but means it metaphorically. The length limit of Tweets brings pros and cons for training a classifier. The benefit is that users are forced to tell a story in a concise way, and the downside is that the lack of clear context may prevent a classifier from well understanding and interpreting the real meaning of a Tweet. Therefore, it is crucial to build an advanced model that can understand the subtle sentiment embedded in Tweets along with their given contexts to make better predictions. Recent advances in deep learning have explored approaches to address these challenges that are commonly seen in other NLP tasks. Convolutional neural networks (CNNs), which have been widely used in numerous computer vision tasks, have also been successfully applied in NLP systems due to their ability for feature extraction and representation. Recurrent neural networks and their popular variants, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are not only suitable for general sequential modeling tasks but also provide the capability to capture long dependency information between words in a sentence. In addition, LSTM and GRU can well address the gradient explosion and vanishing issue and allow a training algorithm to converge. Another breakthrough architecture is Bidirectional Encoder Representations from Transformers (BERT), which stacks layers of Transformer encoders with a multi-headed attention mechanism to enhance a model's ability to capture contextual information. Inspired by these prior efforts, we propose a learning pipeline named SentiBERT-BiLSTM-CNN for disaster prediction based on Tweets.