Understanding what leads to emotions during large-scale crises is important
as it can provide groundings for expressed emotions and subsequently improve
the understanding of ongoing disasters. Recent approaches trained supervised
models to both detect emotions and explain emotion triggers (events and
appraisals) via abstractive summarization. However, obtaining timely and
qualitative abstractive summaries is expensive and extremely time-consuming,
requiring highly-trained expert annotators. In time-sensitive, high-stake
contexts, this can block necessary responses. We instead pursue unsupervised
systems that extract triggers from text. First, we introduce CovidET-EXT,
augmenting (Zhan et al. 2022)'s abstractive dataset (in the context of the
COVID-19 crisis) with extractive triggers. Second, we develop new unsupervised
learning models that can jointly detect emotions and summarize their triggers.
Our best approach, entitled Emotion-Aware Pagerank, incorporates emotion
information from external sources combined with a language understanding
module, and outperforms strong baselines. We release our data and code at
this https URL

本文在研究大规模危机期间情感触发因素的基础上，提出了一种利用无监督学习模型进行情感检测和摘要的新方法，并在 COVID-19 危机下进行了验证

无监督情感触发器摘要提取

Unsupervised Extractive Summarization of Emotion Triggers

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.

本论文使用多语言句子嵌入的神经网络对欧洲 COVID-19 疫情爆发期间收集的 Twitter 消息进行情感分析，并将结果按来源国分离，通过与国家事件的相关性进行时间性发展的相关性分析，研究疫情对人们情绪的影响。

COVID-19 疫情期间欧洲推特跨语言情感分析

Cross-language sentiment analysis of European Twitter messages duringthe  COVID-19 pandemic

Using the eigenvalues and eigenvectors of correlations matrices of some of
the main financial market indices in the world, we show that high volatility of
markets is directly linked with strong correlations between them. This means
that markets tend to behave as one during great crashes. In order to do so, we
investigate several financial market crises that occurred in the years 1987
(Black Monday), 1989 (Russian crisis), 2001 (Burst of the dot-com bubble and
September 11), and 2008 (Subprime Mortgage Crisis), which mark some of the
largest downturns of financial markets in the last three decades.

通过世界一些主要金融市场指数的相关矩阵的特征值及特征向量，我们发现市场行为的高波动性与它们之间的强相关性成正比。这意味着在大规模崩盘期间，市场倾向于表现为同一趋势。为了支持这种说法，我们研究了 1987 年（黑色星期一）、1989 年（俄罗斯危机）、2001 年（互联网泡沫破裂和 9/11 事件）以及 2008 年（次贷危机）等金融市场危机，这些危机是过去三十年中最大的市场下滑。