Li Shanshan, Sun Xiaoling
Institute of Disaster Prevention, School of Information Engineering, Langfang, Hebei, China.
PeerJ Comput Sci. 2023 Jun 16;9:e1417. doi: 10.7717/peerj-cs.1417. eCollection 2023.
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.
自然灾害通常具有突发性和不可预测性，因此很难对其进行推断。通过社交媒体减少突发自然灾害对经济和社会的影响，是控制灾害舆情并在灾后进行重建的一种非常有效的方法。因此，我们提出一种基于社交媒体传播的公众情绪特征提取方法，以实现对自然灾害舆情的智能分析。首先，我们提供一种基于情感特征的舆情分析方法，该方法利用特征提取和Transformer技术来感知舆情样本中的情感。然后，利用提取的特征对公众情绪进行智能识别，实现自然灾害中公众情绪的收集。最后，通过收集到的情感信息，获取公众在自然灾害中的需求，实现基于社交媒体传播的自然灾害舆情分析系统。实验表明，我们的算法能够以90.54%的准确率识别自然灾害舆情类别。此外，我们的自然灾害舆情分析系统能够逐点解构自然灾害的现状，实时掌握灾情。
