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        <title>I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic</title>
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    <abstract>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 -\textgreater anger, (3) optimism -\textgreater 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.</abstract>
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%0 Conference Proceedings
%T I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic
%A Ng, Hui Xian Lynnette
%A Lee, Roy Ka-Wei
%A Awal, Md Rabiul
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ng-etal-2020-miss
%X 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 -\textgreater anger, (3) optimism -\textgreater 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.
%R 10.18653/v1/2020.nlpcss-1.5
%U https://aclanthology.org/2020.nlpcss-1.5
%U https://doi.org/10.18653/v1/2020.nlpcss-1.5
%P 41-49

Markdown (Informal)

[I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic](https://aclanthology.org/2020.nlpcss-1.5) (Ng et al., NLP+CSS 2020)

• I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic (Ng et al., NLP+CSS 2020)

ACL

• Hui Xian Lynnette Ng, Roy Ka-Wei Lee, and Md Rabiul Awal. 2020. I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic. In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 41–49, Online. Association for Computational Linguistics.