Indonesia’s Economic Recovery Post Covid-19 Pandemic Sentiment Analysis

Erfina, Adhitia; Fitina, Lakoa; Hartanto, Prasetyo; Saepudin, Sudin; Rahmalenia, Diah; Maulinda, Dini; Octafiani, Fauziah Nur

doi:10.1109/icced56140.2022.10010695

Cited by 3 publications

(3 citation statements)

References 8 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%

“…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]. A study was conducted to learn public sentiment towards the government for efforts to restore the economy in Indonesia and suggested a high percentage margin between positive and negative sentiments of 37% [11].…”

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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“…The results obtained for sentiment classification using Naive Bayes Classifier on public tweets have achieved maximum expectations. (3)…”

Section: Introductionmentioning

confidence: 99%

Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language

Erfina

Nurul

2023

Data and Metadata

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ChatGPT (Generative Pre-Trained Transformer) is a chatbot that is being widely used by the public. This technology is based on Artificial Intelligence and is capable of having conversational interactions with its users just like humans, but in the form of automated text. Because of this capability, online forums such as Brainly and the like can be overtaken by these smart chatbots. Therefore, this study was conducted to determine the positive and negative sentiments towards ChatGPT using Naive Bayes Classification algorithm on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had a positive sentiment of 57.6% towards ChatGPT, while the negative sentiment reached 42.4%. The resulting classification model had an accuracy of 80%, indicating a good classification model in determining sentiment probabilities. These findings provide a basis for the development of better AI chatbot technology that can meet user needs. The results of this study provide insights into user sentiment towards ChatGPT and can be used as a reference for future AI chatbot development.

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