Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Prastyo, Pulung Hendro; Sumi, Amin Siddiq; Dian, Ade Widyatama; Permanasari, Adhistya Erna

doi:10.20473/jisebi.6.2.112-122

Cited by 60 publications

(48 citation statements)

References 24 publications

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“…This policy received much criticism and was considered a 'disaster' because, subsequently, the number of COVID-19 cases in Indonesia continued to increase [21]. In the public's sentiment, although most Indonesians are satisfied with the government approach in dealing with the COVID-19's economic impacts, they criticize the government's overall performance in handling the pandemic [22].…”

Section: B Predicting Covid-19mentioning

confidence: 99%

The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia

Auliya

Wulandari

2021

JISEBI

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Background: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly across the world and infected millions of people, many of whom died. As part of the response plans, many countries have been attempting to restrict people’s mobility by launching social distancing protocol, including in Indonesia. It is then necessary to identify the campaign’s impact and analyze the influence of mobility patterns on the pandemic’s transmission rate.Objective: Using mobility data from Google and Apple, this research discovers that COVID-19 daily new cases in Indonesia are mostly related to the mobility trends in the previous eight days.Methods: We generate ten-day predictions of COVID-19 daily new cases and Indonesians’ mobility by using Long-Short Term Memory (LSTM) algorithm to provide insight for future implementation of social distancing policies.Results: We found that all eight-mobility categories result in the highest accumulation correlation values between COVID-19 daily new cases and the mobility eight days before. We forecast of the pandemic daily new cases in Indonesia, DKI Jakarta and worldwide (with error on MAPE 6.2% - 9.4%) as well as the mobility trends in Indonesia and DKI Jakarta (with error on MAPE 6.4 - 287.3%).Conclusion: We discover that the driver behind the rapid transmission in Indonesia is the number of visits to retail and recreation, groceries and pharmacies, and parks. In contrast, the mobility to the workplaces negatively correlates with the pandemic spread rate.

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Section: B Predicting Covid-19mentioning

confidence: 99%

The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia

Auliya

Wulandari

2021

JISEBI

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“…The spectrum of polarity usually consists of three types: positive, neutral, and negative. Dina [23] and Prastyo et al [24] divided the polarity into three types.…”

Section: Text Miningmentioning

confidence: 99%

Measuring User Satisfaction of Educational Service Applications using Text Mining and Multicriteria Decision-Making Approach

Dina

Yunardi

Firdaus

et al. 2021

Int. J. Emerg. Technol. Learn.

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Rapid growth of educational technology services today means that there are more applications in the market. Users may find it hard to choose the most suitable application, so they look for references. Experience shared in the form of text reviews and numerical rating can provide references. Text re-views are particularly specific and so they can provide insights to user satis-faction. In this study, we use text mining and multicriteria decision-making approach to measure the user satisfaction. The data is crawled and collected from seven educational applications: Coursera, edX, Khan Academy, LinkedIn Learning, Quipper, Socratic and Udemy. Nine attributes are used to measure the user reviews according to quality model of e-learning sys-tems. The result is in favor of Khan Academy, while Quipper is ranked the lowest. The v-values used range between 0 and1 and what is unique is that the rank of Khan Academy and Quipper are not affected by v-value while the ranks of the other applications are. It indicates that Khan Academy has high user satisfaction in terms of utility and low complaint from individuals. Quipper shows the opposite.

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“…Twitter is one of the most popular social media platforms, and tweets reveal the government's progress in fighting the pandemic more in real time. In order to investigation the impact on the public of pandemic management of government, many researches conduct the emotional analysis of different type of tweets published by government and concluded the public's different attitude on pandemic prevention policies and measures of government [1][2]. Also, researchers are interest in the diffusion law of public information, such as the diffusion of academic result [3], conspiracy [4], topics about COVID-19 [5] and emotion through social networks [6].…”

Section: Introductionmentioning

confidence: 99%

“…All the corpus is available at https://github.com/xihaixu/EmotionDiffusionofCOVID-19 2. https://github.com/DocNow/twarc 3 https://www.nltk.org/…”

mentioning

confidence: 99%

Public Emotional Diffusion over COVID-19 Related Tweets Posted by Major Public Health Agencies in the United States

et al. 2022

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Since the end of 2019, the ongoing of COVID-19 outbreak worldwide not only challenges the management capacity of governments on the public health emergency, but also tests the management capacity of governments on the public opinion and the governance capacity of dealing with social emergencies. To understand the impact on public emotion over COVID-19 related tweets posted by the major public health agency in the United States, this paper study the process and characteristics of public emotional diffusion in the tweets network by taking the four-official Twitter users of the public health system in the United States as an example. In this paper, we extract the interactions between tweets in the COVID-19-TweetIds dataset, draw the tweets diffusion network, propose a method to measure the characteristics of the emotional diffusion network, analyze the changes of the public emotional intensity and the proportion of emotional polarity, investigate the emotional influence of key nodes and users in the process of tweet emotional diffusion, and study the emotional diffusion of tweets of different tweeting time periods, topics and institutions. The results show that the emotional polarity of tweets has changed from negative to positive with the improvement of pandemic management measures. The public's emotional polarity on pandemic related topics tends to be negative, and the emotional intensity of management measures such as pandemic medical services turn from positive to negative to the greatest extent, while the emotional intensity of pandemic related knowledge changes the most. The tweets posted by the Centers for Disease Control and Prevention and the Food and Drug Administration of the United States have a broad impact on public emotions, and the emotional spread of tweets' polarity eventually forms a very close proportion of opposite emotions.

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Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Cited by 60 publications

References 24 publications

The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia

The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia

Measuring User Satisfaction of Educational Service Applications using Text Mining and Multicriteria Decision-Making Approach

Public Emotional Diffusion over COVID-19 Related Tweets Posted by Major Public Health Agencies in the United States

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