@Article{info:doi/10.2196/41517,
author="Maghsoudi, Arash
and Nowakowski, Sara
and Agrawal, Ritwick
and Sharafkhaneh, Amir
and Kunik, Mark E
and Naik, Aanand D
and Xu, Hua
and Razjouyan, Javad",
title="Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre-- and Peri--COVID-19 Pandemic Retrospective Study",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="27",
volume="24",
number="12",
pages="e41517",
keywords="COVID-19; coronavirus; sleep; Twitter; natural language processing; sentiment analysis; transformers; Dempster-Shafer theory; sleeping; social media; pandemic; effect; viral infection",
abstract="Background: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. Objective: In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. Methods: We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. Results: We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84{\%} accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95{\%} CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21{\%} higher than that before midnight (OR 1.21, 95{\%} CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2{\%} higher after midnight compared to those before midnight (OR 1.02, 95{\%} CI 1.00-1.07; P=.008), they were 43{\%} higher (OR 1.43, 95{\%} CI 1.40-1.46; P<.001) in the peripandemic interval. Conclusions: The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep. ",
issn="1438-8871",
doi="10.2196/41517",
url="https://www.jmir.org/2022/12/e41517",
url="https://doi.org/10.2196/41517",
url="http://www.ncbi.nlm.nih.gov/pubmed/36417585"
}