I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

Hui Xian Lynnette Ng, Roy Ka-Wei Lee, Md Rabiul Awal


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 -> 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.
Anthology ID:
2020.nlpcss-1.5
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–49
Language:
URL:
https://aclanthology.org/2020.nlpcss-1.5
DOI:
10.18653/v1/2020.nlpcss-1.5
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.nlpcss-1.5.pdf
Video:
 https://slideslive.com/38940603