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

Future Internet

Guizhe Song¹  Degen Huang¹ 

[1] School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China;

关键词: natural language processing;    text classification;    mining information;    Tweet data;    social media;   

DOI  :  10.3390/fi13070163

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown