Sentiment Analysis on Twitter-Based Teleworking in a Post-Pandemic COVID-19 Context

Rojas Rincón, Joan Sebastián; Riveros Tarazona, Andrés Ricardo; Mejía Martínez, Andrés Mauricio; Acosta-Prado, Julio César

doi:10.3390/socsci12110623

Cited by 2 publications

(3 citation statements)

References 59 publications

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“…While there exist studies that examine sentiments related to the pandemic (see Introduction), they often focus on a single snapshot in time during the pandemic or on a highly specific domain or topic in the post-pandemic period. While there are no existing studies for a direct side-by-side comparison with our results, the overall trend in mental recovery post-2020, as indicated by our study, aligns with findings in positive attitudes toward different topics in several studies conducted in 2022 and 2023 (e.g., [1,[8][9][10][11]14]).…”

Section: Discussionsupporting

confidence: 90%

“…Sentiment analysis of Twitter-based teleworking in a post-pandemic context showed the prevalence of positive sentiments regarding telework that were generally associated with confidence, anticipation, and joy. [9]. Social media data were collected to provide a comprehensive analysis of logistics and transportation trends and showed The overall sentiment toward post-pandemic logistics in Japan was positive [10].…”

Section: Introductionmentioning

confidence: 99%

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Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022

Yan,

Liu

2024

PLoS ONE

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Background As the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people’s emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre-pandemic level. Method We collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post-emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed-effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment. Results Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values. Conclusions Our study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022

Yan,

Liu

2024

PLoS ONE

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“…Data in the form of comments is processed using sentiment analysis so that the percentage of sentiment is visible, whether neutral, positive or negative. It was also done by [28] who revealed sentiment result of Twitter by sentiment analysis. [29] did something similar, namely that data on social media in the form of photos, text, video and audio can be valuable information [29].…”

Section: Methodsmentioning

confidence: 99%

How Do Online Reviews on YouTube Construct Perception?

Fatmasari,

Febrianto,

Nugroho

2023

J.Adman

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Online reviews are a step to communicate various brands or products. Product reviews usually contain product features, advantages, and disadvantages, as well as other products for comparison. Electronic product reviews conducted online on YouTube channels are the object of this research. With certain criteria, five videos were obtained as review objects that will be studied in this research. The 458 dataset of audience comments on this review will be studied qualitatively using machine learning in the form of software of Nvivo, as well as internet ethnography and pragmatics. From the analysis results, it is known that each electronic product review video provides positive, negative or neutral perceptions and attitudes from the audience. Audience sentiment is formed from product features, images of reviewers, how it was reviewed, and product image. By the limitation of the research, it needs to be developed further with different methods and objects to provide more input to producers or reviewers.

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Sentiment Analysis on Twitter-Based Teleworking in a Post-Pandemic COVID-19 Context

Cited by 2 publications

References 59 publications

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022

How Do Online Reviews on YouTube Construct Perception?

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