An Emotion Care Model using Multimodal Textual Analysis on COVID-19.
An Emotion Care Model using Multimodal Textual Analysis on COVID-19.
复制标题
基于多模态文本分析的COVID-19情感关怀模型
DOI：
10.1016/j.chaos.2021.110708
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Ferrara M
中科院分区：
文献类型：
--
作者：
Gupta V;Jain N;Katariya P;Kumar A;Mohan S;Ahmadian A;Ferrara M
于二零二零年年初，全球遭受重大疫情COVID-19的打击，令整个地球遭受创伤。传染病蔓延迅速，迫使政策制定者和政府采取封锁措施。封锁进一步迫使人们被软禁，这进一步导致社交媒体平台上的情绪爆发。对政府和政策制定者来说，在这些时候感知人们的情绪状态变得至关重要和具有战略意义。在这方面，本文提出了一种新的情感护理方案，用于分析与COVID-19相关的实时推文中包含的多模态文本数据。此外，本文还研究了8种情绪（愤怒，预期，厌恶，恐惧，喜悦，悲伤，惊讶和信任），涉及自然，封锁，健康，教育，市场和政治等多个类别。就我们所知，这是第一次对与这一流行病有关的多种模式进行语言分析。以印度为例，我们从文本分析中推断，由于污染明显减少，除了自然（约17%）之外，“喜悦”对一切都较少（约9-15%）。由于教师联谊会的不断努力，教育系统需要更多的信任（~29%）。卫生部门发现，悲伤（~16%）和恐惧（~18%）是人们在生命受到威胁时的主要情绪。此外，还提供了状态和情感描述。还为此开发了一个交互式互联网应用程序。
At the dawn of the year 2020, the world was hit by a significant pandemic COVID-19, that traumatized the entire planet. The infectious spread grew in leaps and bounds and forced the policymakers and governments to move towards lockdown. The lockdown further compelled people to stay under house arrest, which further resulted in an outbreak of emotions on social media platforms. Perceiving people's emotional state during these times becomes critically and strategically important for the government and the policymakers. In this regard, a novel emotion care scheme has been proposed in this paper to analyze multimodal textual data contained in real-time tweets related to COVID-19. Moreover, this paper studies 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) over multiple categories such as nature, lockdown, health, education, market, and politics. This is the first of its kind linguistic analysis on multiple modes pertaining to the pandemic to the best of our understanding. Taking India as a case study, we inferred from this textual analysis that ‘joy’ has been lesser towards everything (~9-15%) but nature (~17%) due to the apparent fact of lessened pollution. The education system entailed more trust (~29%) due to teachers' fraternity's consistent efforts. The health sector witnessed sadness (~16%) and fear (~18%) as the dominant emotions among the masses as human lives were at stake. Additionally, the state-wise and emotion-wise depiction is also provided. An interactive internet application has also been developed for the same.
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影响因子：
11.3
作者：
Cao, Wenjun;Fang, Ziwei;Zheng, Jianzhong
通讯作者：
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影响因子：
3.7
作者：
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影响因子：
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作者：
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DOI：
10.1101/lm.044115.116
发表时间：
2017-09
期刊：
Learning & memory (Cold Spring Harbor, N.Y.)
影响因子：
--
作者：
Garcia R
通讯作者：
Garcia R
影响因子：
3.9
作者：
Gao, Wei;Veeresha, Pundikala;Baskonus, Haci Mehmet
通讯作者：
Baskonus, Haci Mehmet