I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic
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我想你宝贝：分析 COVID-19 大流行期间的情绪动态
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Rabiul Awal
中科院分区：
文献类型：
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作者：
Hui Xian Lynnette Ng;R. Lee;Rabiul Awal
随着全球因COVID-19大流行而处于封锁状态，本文研究了Twitter上表达的情绪。本研究采用时间序列分析的组合策略，对推文主题增强的情绪进行分析，从而深入了解大流行期间的情绪转变。在用主导情感和主题注释推文之后，使用时间序列情感分析来识别厌恶和愤怒作为最常见的识别情感。通过对每个用户的纵向分析，我们构建了情绪转换图，观察厌恶和愤怒之间的关键转换，以及愤怒和厌恶情绪状态中的自我转换。通过对用户纵向分析的聚类来观察用户模式，揭示了情感转变分为四个主要聚类：（1）短时间内的不稳定运动，（2）厌恶->愤怒，（3）乐观->快乐。(4)长时间不稳定的运动。最后，通过构建情感话题隐马尔可夫模型，利用话题信息增加情感转移状态，提出了一种预测用户后续话题，进而预测用户情感的方法。结果表明，预测结果优于基线，刺激了基于Twitter帖子预测情绪状态的方向。
With the world on a lockdown due to the COVID-19 pandemic, this paper studies emotions expressed on Twitter. Using a combined strategy of time series analysis of emotions augmented by tweet topics, this study provides an insight into emotion transitions during the pandemic. After tweets are annotated with dominant emotions and topics, a time-series emotion analysis is used to identify disgust and anger as the most commonly identified emotions. Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states. Observing user patterns through clustering of user longitudinal analyses reveals emotional transitions fall into four main clusters: (1) erratic motion over short period of time, (2) disgust -> anger, (3) optimism -> joy. (4) erratic motion over a prolonged period. Finally, we propose a method for predicting users subsequent topic, and by consequence their emotions, through constructing an Emotion Topic Hidden Markov Model, augmenting emotion transition states with topic information. Results suggests that the predictions fare better than baselines, spurring directions of predicting emotional states based on Twitter posts.