论文标题
关于新颖冠状病毒或COVID-19在线讨论的深度情感分类和主题发现：使用LSTM复发性神经网络方法的NLP
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
论文作者
论文摘要
互联网论坛和公共社交媒体（例如在线医疗保健论坛）为用户（人/患者）提供了一个方便的渠道，以讨论健康问题，以讨论和共享信息。据报道，2019年12月下旬，据报道，一种新型冠状病毒（感染导致了该疾病的感染），由于病毒在世界其他地区的迅速传播，世界卫生组织宣布了紧急状态。在本文中，我们使用社交媒体中的COVID-19的自动提取与基于主题建模的自然语言过程（NLP）方法的自动提取，从公众意见中揭示了与Covid-19相关的各种问题。此外，我们还调查了如何使用LSTM复发性神经网络进行COVID-19评论的情感分类。我们的发现阐明了使用公众意见和合适的计算技术来了解Covid-19的问题并指导相关决策的重要性。
Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.
