论文标题
欧洲Twitter消息的跨语言情感分析在COVID-19大流行期间
Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic
论文作者
论文摘要
社交媒体数据可能是危机期间非常重要的信息来源。在此期间，用户生成的消息为人们提供了一个窗口，使我们对他们的情绪和意见有所了解。由于大量此类信息，对人口范围的发展进行了大规模分析。在本文中，我们分析了在欧洲的Covid-19大流行期间收集的Twitter消息（推文）。这是通过使用多语言句子嵌入的神经网络来实施的。我们按原产国分开结果，并将其时间发展与这些国家的事件相关联。这使我们能够研究情况对人们心情的影响。例如，我们看到，锁定公告与几乎所有被调查国家的情绪恶化相关，后者在短时间内恢复。
Social media data can be a very salient source of information during crises. User-generated messages provide a window into people's minds during such times, allowing us insights about their moods and opinions. Due to the vast amounts of such messages, a large-scale analysis of population-wide developments becomes possible. In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment. This is implemented with a neural network for sentiment analysis using multilingual sentence embeddings. We separate the results by country of origin, and correlate their temporal development with events in those countries. This allows us to study the effect of the situation on people's moods. We see, for example, that lockdown announcements correlate with a deterioration of mood in almost all surveyed countries, which recovers within a short time span.
