Monitoring of Public Opinion on Typhoon Disaster Using Improved Clustering Model Based on Single-Pass Approach

Ambiguities in information and difficulties in distinguishing truth from fiction during natural disasters produce negative emotions and create problems in emergency rescue work. In this study, we focused on two aspects. First, we propose a method that improves upon the existing streaming data clustering method based on twin networks, which is a single-pass topic clustering method based on the Siamese-bidirectional gated recurring units (BiGRUs)-attention technique. Second, a bidirectional encoder representation from transformers (BERT)-BiGRU-conditional random field (CRF) sentiment analysis model based on the idea of sequence tagging was designed. Combining this method with the proposed topic clustering method, we propose a new disaster management method that analyzes the public opinion before and after a disaster. We conducted experiments that showed that the single-pass topic clustering model based on Siamese-BiGRU-attention outperformed other clustering methods in terms of clustering performance. Simultaneously, the BERT-BiGRU-CRF model was employed to statistically analyze data on daily public opinion monitoring. The statistics of the clustering results before and after disasters occur and the emotion distribution based on each category were obtained. Overall, the proposed method can help rescue workers and governmental officials understand the sentiments of the public more clearly and provide the necessary response measures more effectively during disasters.