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Article

A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data

School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
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Author to whom correspondence should be addressed.
Academic Editors: Massimo Esposito, Giovanni Luca Masala, Aniello Minutolo and Marco Pota
Future Internet 2021, 13(7), 163; https://doi.org/10.3390/fi13070163
Received: 21 May 2021 / Revised: 13 June 2021 / Accepted: 21 June 2021 / Published: 25 June 2021
(This article belongs to the Special Issue Natural Language Engineering: Methods, Tasks and Applications)
The massive amount of data generated by social media present a unique opportunity for disaster analysis. As a leading social platform, Twitter generates over 500 million Tweets each day. Due to its real-time characteristic, more agencies employ Twitter to track disaster events to make a speedy rescue plan. However, it is challenging to build an accurate predictive model to identify disaster Tweets, which may lack sufficient context due to the length limit. In addition, disaster Tweets and regular ones can be hard to distinguish because of word ambiguity. In this paper, we propose a sentiment-aware contextual model named SentiBERT-BiLSTM-CNN for disaster detection using Tweets. The proposed learning pipeline consists of SentiBERT that can generate sentimental contextual embeddings from a Tweet, a Bidirectional long short-term memory (BiLSTM) layer with attention, and a 1D convolutional layer for local feature extraction. We conduct extensive experiments to validate certain design choices of the model and compare our model with its peers. Results show that the proposed SentiBERT-BiLSTM-CNN demonstrates superior performance in the F1 score, making it a competitive model in Tweets-based disaster prediction. View Full-Text
Keywords: natural language processing; text classification; mining information; Tweet data; social media natural language processing; text classification; mining information; Tweet data; social media
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MDPI and ACS Style

Song, G.; Huang, D. A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data. Future Internet 2021, 13, 163. https://doi.org/10.3390/fi13070163

AMA Style

Song G, Huang D. A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data. Future Internet. 2021; 13(7):163. https://doi.org/10.3390/fi13070163

Chicago/Turabian Style

Song, Guizhe, and Degen Huang. 2021. "A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data" Future Internet 13, no. 7: 163. https://doi.org/10.3390/fi13070163

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