论文标题
一个深层语言独立的网络，用于通过情感分析分析Covid-19对世界的影响
A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis
论文作者
论文摘要
到2019年底，武汉经历了新的冠状病毒爆发，这些冠状病毒很快在世界各地蔓延，导致致命的大流行感染了全球数百万的人。政府和公共卫生机构遵循了许多应对致命病毒的策略。但是，该病毒严重影响了人们的社会和经济生活。在本文中，我们提取并研究了该病毒受影响最严重的国家的人们的意见，即美国，巴西，印度，俄罗斯和南非。我们提出了一个深层与语言无关的基于注意力集中的Cons-Bigru网络（MacBig-NET），其中包括嵌入层，单词级别的编码注意力以及句子级的编码注意机制，以提取正面，负面和中性的情感。嵌入层将句子序列编码为实价矢量。单词级别和句子级的编码是由基于1D的 - bigru的机制执行的，其次是单词级别和句子级别的关注。我们通过从Twitter上爬推文来进一步开发一个Covid-19的情感数据集。我们提出的数据集的广泛实验证明了提出的Macbig-NET的有效性。此外，注意力重量的可视化和深入的结果分析表明，所提出的网络有效地捕捉了人们的情绪。
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.
