Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread all over the world, resulting in a deadly pandemic that infected millions of people around the globe. The government and public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the social and economic lives of the people. In this paper, we extract and study the opinion of people from the top five worst affected countries by the virus, namely USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanism to extract the positive, negative, and neutral sentiments. The embedding layer encodes the sentence sequence into a real-valued vector. The word-level and sentence-level encoding is performed by a 1D Conv-BiGRU based mechanism, followed by word-level and sentence-level attention, respectively. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter. Extensive experiments on our proposed dataset demonstrate the effectiveness of the proposed MACBiG-Net. Also, attention-weights visualization and in-depth results analysis shows that the proposed network has effectively captured the sentiments of the people.

中文翻译：

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一个独立于深度语言的网络，可通过情感分析来分析COVID-19对世界的影响

到2019年底，武汉经历了新型冠状病毒的爆发，并很快在世界范围内传播，导致致命的大流行，感染了全球数百万人。政府和公共卫生机构采取了许多策略来对抗致命病毒。但是，该病毒严重影响了人们的社会和经济生活。在本文中，我们提取并研究了受病毒影响最严重的五个国家（美国，巴西，印度，俄罗斯和南非）的观点。我们提出了一种基于语言的，与多语言无关的，基于多层注意力的Conv-BiGRU网络（MACBiG-Net），该网络包括嵌入层，单词级别的编码注意和句子级别的编码注意机制，以提取积极，消极和中立的情感。嵌入层将句子序列编码为实值向量。单词级别和句子级别的编码是通过基于1D Conv-BiGRU的机制执行的，然后分别进行单词级别和句子级别的关注。通过抓取Twitter上的推文，我们进一步开发了COVID-19情感数据集。在我们提出的数据集上进行的大量实验证明了提出的MACBiG-Net的有效性。此外，注意力权重的可视化和深入的结果分析表明，拟议的网络有效地捕捉了人们的情感。在我们提出的数据集上进行的大量实验证明了提出的MACBiG-Net的有效性。此外，注意力权重的可视化和深入的结果分析表明，拟议的网络有效地捕捉了人们的情感。在我们提出的数据集上进行的大量实验证明了提出的MACBiG-Net的有效性。此外，注意力权重的可视化和深入的结果分析表明，拟议的网络有效地捕捉了人们的情感。