One of the most important incidents in the world in 2020 is the outbreak of the Coronavirus. Users on social networks publish a large number of comments about this event. These comments contain important hidden information of public opinion regarding this pandemic. In this research, a large number of Coronavirus-related tweets are considered and analyzed using natural language processing and information retrieval science. Initially, the location of the tweets is determined using a dictionary prepared through the Geo-Names geographic database, which contains detailed and complete information of places such as city names, streets, and postal codes. Then, using a large dictionary prepared from the terms of economics, related tweets are extracted and sentiments corresponded to tweets are analyzed with the help of the RoBERTa language-based model, which has high accuracy and good performance. Finally, the frequency chart of tweets related to the economy and their sentiment scores (positive and negative tweets) is plotted over time for the entire world and the top 10 economies. From the analysis of the charts, we learn that the reason for publishing economic tweets is not only the increase in the number of people infected with the Coronavirus but also imposed restrictions and lockdowns in countries. The consequences of these restrictions include the loss of millions of jobs and the economic downturn.
翻译:2020年世界上最重要的事件之一是Corona病毒的爆发; 社交网络的用户公布大量关于这一事件的评论; 这些评论含有关于这一流行病的公众意见的重要隐藏信息; 在这项研究中,利用自然语言处理和信息检索科学,审议和分析了大量与Corona病毒有关的推特; 最初,推文的位置是通过地理名称地理数据库编写的字典确定的,该词典载有城市名称、街道和邮政编码等地点的详细和完整信息; 然后,利用从经济条件出发的大型字典,提取相关的推文,并在基于RoBERTA语言的模式的帮助下分析与推文对应的情绪,该模式具有很高的准确性和良好性能; 最后,与经济有关的推文的频率图及其情绪分数(正负推文)是随着时间的推移为全世界和十大经济体绘制的。 从对图表的分析中,我们了解到,发布经济推文的原因不仅仅是受科罗纳病毒感染的人数的增加,而且还在各国施加了数百万个工作损失的后果。