# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 5 Conclusions  
Disaster analysis is highly related to people's daily lives, and recent years have seen more research efforts dedicating to this field. Research on disaster prediction helps augment people's awareness, improve the mechanism of a government rescue, and schedule charitable institutions' work. This paper investigates a novel model for disaster detection using Tweets. Our model, SentiBERT-BiLSTM-CNN, leverages a sentiment-aware BERT encoder, an attentive BiLSTM, and a 1D convolutional layer to extract high-quality linguistic features for disaster prediction. The model is validated through extensive experiments compared to its peers, making it a competitive model for building a real-time disaster detector.  
Although the proposed model is trained and validated on an English dataset, it can be applied to datasets in other languages. Specifically, in a different language environment, the following adjustments need to be made: first, we should find a BERT model pre-trained in the target language or in a multi-lingual setting, which is readily available online ([[https://huggingface.co/transformers/pretrained_models.html](https://huggingface.co/transformers/pretrained_models.html)]([https://huggingface.co/transformers/pretrained_models.html](https://huggingface.co/transformers/pretrained_models.html)), accessed on 12 March 2021); second, we need to retrain SentiBERT on a sentiment analysis dataset in the target language; lastly, a new disaster Tweet dataset in the target language is needed to train and validate the model. In this new language environment, SentiBERT can now generate sentiment-aware word embeddings to be consumed by the subsequent BiLSTM and CNN modules, which are language independent.  
This work has the following limitations that also point out the future directions. First, it remains interesting to uncover the role keywords played in disaster detection. Given that keywords like \"blaze\" and \"apocalypse\" can appear in both disaster and non-disaster Tweets, it is challenging to effectively utilize the keywords as extra knowledge to help boost the detection accuracy. One potential solution is to fine-tune BERT through pair-wise training, taking a pair of Tweets containing the same keywords but with opposite training labels; this way, BERT is forced to better understand the contextual difference between two Tweets. Second, it remains unknown that how well the model trained on our dataset performs on other disaster datasets, such as HumAID [54] and Crisismmd [55]; in addition, we expect to obtain a more robust model that is trained across multiple disaster/crisis Tweets datasets. Third, we are interested in creating a multilingual disaster detector that can understand and process Tweets in different languages; it is worth conducting a performance comparison between a multilingual and a monolingual model.  
Conceptualization and methodology, G.S. and D.H.; software, validation, and original draft preparation, G.S.; review and editing, supervision, funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2020 AAA0108004) and the National Natural Science Foundation of China (61672127, U1936109). The funding agency has no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  
Not applicable.  
Data Availability Statement: Data Availability Statement: Natural Language Processing with Disaster Tweets dataset supporting the conclusions of this article are available at [[https://www.kaggle.com/c/nlp-getting-started/data](https://www.kaggle.com/c/nlp-getting-started/data)]([https://www.kaggle.com/c/nlp-getting-started/data](https://www.kaggle.com/c/nlp-getting-started/data)) (accessed on 20 March 2021).  
The authors declare no conflict of interest.