Application of public emotion feature extraction algorithm based on social media communication in public opinion analysis of natural disasters

Abstract

Natural disasters are usually sudden and unpredictable, so it is too difficult to infer them. Reducing the impact of sudden natural disasters on the economy and society is a very effective method to control public opinion about disasters and reconstruct them after disasters through social media. Thus, we propose a public sentiment feature extraction method by social media transmission to realize the intelligent analysis of natural disaster public opinion. Firstly, we offer a public opinion analysis method based on emotional features, which uses feature extraction and Transformer technology to perceive the sentiment in public opinion samples. Then, the extracted features are used to identify the public emotions intelligently, and the collection of public emotions in natural disasters is realized. Finally, through the collected emotional information, the public’s demands and needs in natural disasters are obtained, and the natural disaster public opinion analysis system based on social media communication is realized. Experiments demonstrate that our algorithm can identify the category of public opinion on natural disasters with an accuracy of 90.54%. In addition, our natural disaster public opinion analysis system can deconstruct the current situation of natural disasters from point to point and grasp the disaster situation in real-time.

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

“…This category includes studies focusing on analyzing public sentiment and perceptions towards disasters or crisis situations. The research works in [19,[41][42][43][44][45] fall within this category, as shown in Table 5 (detailed in Appendix A, Table A2). Notably, models such as BERT demonstrate remarkable accuracy in classifying sentiments towards COVID-19 vaccines and reporting symptoms, leveraging contextual embeddings for nuanced understanding [19,42].…”

“…However, challenges such as lower performance exhibited by fixed embeddings, as evidenced by Word2Vec, underscore the importance of employing adaptable models to capture the contextual nuances effectively [42]. Deep Learning [19,41,44] High accuracy in detecting beliefs, opinions, and emotions, showcasing effective analysis.…”