Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Rahardi, Majid; Aminuddin, Afrig; Abdulloh, Ferian Fauzi; Nugroho, Rizky Adhi

doi:10.14569/ijacsa.2022.0130665

Cited by 23 publications

(16 citation statements)

References 23 publications

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“…However, while such tasks benefit from the diverse interpretations of a broad workforce, this can also lead to a lack of consistency, with disparate annotators potentially interpreting identical instructions differently. Thus, MTurk can be a powerful tool for human labeling, but like any method, it has its trade-offs and may not be suitable for all projects ( 43 , 46 , 47 ).…”

Section: Related Workmentioning

confidence: 99%

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model

Aldosery,

Carruthers,

Kay

et al. 2024

Front. Public Health

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IntroductionTo protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response.MethodsIn this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model.ResultsThe results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements.DiscussionWhile much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public’s stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest.

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Section: Related Workmentioning

confidence: 99%

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model

Aldosery,

Carruthers,

Kay

et al. 2024

Front. Public Health

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“…In classification studies based on the principle of least intrinsic risk, it creates a hyperplane for the separation between classes. In the hyperplane determines in which class the new sample will be placed [25]. SVM excel at both linear and nonlinear classification tasks [22].…”

Section: Support Vector Machinementioning

confidence: 99%

Sentiment analysis of coronavirus data with ensemble and machine learning methods

Başarslan,

Kayaalp

2024

Turkish Journal of Engineering

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The coronavirus pandemic has distanced people from social life and increased the use of social media. People's emotions can be determined with text data collected from social media applications. This is used in many fields, especially in commerce. This study aims to predict people's sentiments about the pandemic by applying sentiment analysis to Twitter tweets about the pandemic using single machine learning classifiers (Decision Tree-DT, K-Nearest Neighbor-KNN, Logistic Regression-LR, Naïve Bayes-NB, Random Forest-RF) and ensemble learning methods (Majority Voting (MV), Probabilistic Voting (PV), and Stacking (STCK)). After vectorizing the tweets using two predictive methods, Word2Vec (W2V) and Doc2Vec, and two traditional word representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BOW), classification models built using single machine learning classifiers were compared to models built using ensemble learning methods (MV, PV and STCK) by heterogeneously combining single machine classifier algorithms. Accuracy (ACC), F-measure (F), precision (P), and recall (R) were used as performance measures, with training/test separation rates of 70%-30% and 80%-20%, respectively. Among these models, the ACC of ensemble learning models ranged from 89% to 73%, while the ACC of single classifier models ranged from 60% to 80%. Among the ensemble learning methods, STCK with Doc2Vec text representation/embedding method gave the best ACC result of 89%. According to the experimental results, ensemble models built with heterogeneous machine learning classifier algorithms gave better results than single machine learning classifier algorithms.

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“…The Support Vector Machine algorithm is one of the algorithms included in the Supervised Learning category, which means that the data used for machine learning is data that has a previous label [18], [19]. So that in the decisionmaking process, the machine will categorize the testing data into labels that are in accordance with its characteristics.…”

Section: Support Vector Machinementioning

confidence: 99%

Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia

Abdulloh¹,

Rahardi²,

Aminuddin³

et al. 2022

IJACSA

Self Cite

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Tracer Study is a mandatory aspect of accreditation assessment in Indonesia. The Indonesian Ministry of Education requires all Indonesia Universities to anually report graduate tracer study reports to the government. Tracer study is also needed by the University in evaluating the success of learning that has been applied to the curriculum. One of the things that need to be evaluated is the level of absorption of graduates into the working industry, so a machine learning model is needed to assist the University Officials in evaluating and understanding the character of its graduates, so that it can help determine curriculum policies. In this research, the researcher focuses on making a reliable machine learning model with a tracer study dataset format that has been determined by the Government of Indonesia. The dataset was obtained from the tracer study of Amikom University. In this study, SVM will be tested with several variants of the algorithm to handle imbalanced data. The study compared SMOTE, SMOTE-ENN, and SMOTE-Tomek combined with SVM to detect the employability of graduates. The test was carried out with K-Fold Cross Validation, with the highest accuracy and precision results produced by SMOTE-ENN SVM model by value of 0.96 and 0.89.

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Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Cited by 23 publications

References 23 publications

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model

Sentiment analysis of coronavirus data with ensemble and machine learning methods

Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia

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