# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 2 Related Work
### Learning-Based Disaster Tweets Detection  
One of the early efforts to identify and classify disaster Tweets is by [PERSON] et al. [6], who focused on the Tweets generated when Hurricane Sandy hit New York in 2012. In [6], six fine-grained categories of Tweets, including Reporting, Sentiment, Information, Action, Preparation, and Movement, are annotated. With a series of hand-crafted features, such as key terms, Bigrams, time, and URLs, the dataset is used to train three feature-based models, including SVM, maximum entropy, and Naive Bayes models. [PERSON] et al. [41] developed a weakly-supervised model based on a bag of words, combined with an online algorithm that helps learn the weights of words to boost detection performance. [PERSON] et al. [42] first transformed Tweets into vectors using count vectorization and Term Frequency-Inverse Document Frequency (TF-IDF), based on a set of pre-identified disaster keywords; the vectorized Tweets are then trained using Naive Bayes, Logistic Regression, J48, Random Forest, and SVM to obtain various classifiers. [PERSON] et al. [43] investigated a Markov model-basedmodel to predict the priority and location of Tweets during a disaster. [PERSON] et al. [44] designed a neural architecture that consists of a CNN to extract features from Tweets and a multilayer perceptron (MLP) to perform classification. [PERSON] [45] developed a BERT-based hybrid model that uses both hand-crafted features and learned ones for informative Tweets identification. [PERSON] et al. [46] investigate a domain-adapted learning task that uses a Naive Bayes classifier, combined with an iterative self-training algorithm, to incorporate annotated data from a source disaster dataset and data without annotation from the target disaster dataset into a classifier for the target disaster. More broadly, prior efforts on event Tweet detection are also of interest. [PERSON] et al. [47] proposed a model named SensorTree to detect protest events by tracking information propagated through the Twitter user communities and monitoring the sudden change in the growth of these communities as burst for event detection. [PERSON] et al. [48] developed a Dynamic Heartbeat Graph (DHG) model to detect trending topics from the Twitter stream. An investigation of recent efforts [49] in disaster Tweet detection reveals a lack of deep learning-based methods that have shown superiority in numerous other NLP applications, as mentioned in Section 2.1. However, in the sub-field of disaster Tweets detection, the use cases are still insufficient. In addition, the idea of integrating sentiment information into a disaster detector remains unexplored, and our study is an attempt to fill this gap.  
Inspired by the prior efforts, we design a learning pipeline that includes a BERT variant named SentiBERT [50] to obtain sentiment-aware contextual embeddings, a BiLSTM layer for sequential modeling, and a CNN for feature extraction. The pipeline aggregates the strength of each individual block to enhance the predictive power that realizes an accurate disaster detector.