Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine
In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Poltekkes Kemenkes Surabaya
2023-09-01
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| Series: | Journal of Electronics, Electromedical Engineering, and Medical Informatics |
| Subjects: | |
| Online Access: | https://jeeemi.org/index.php/jeeemi/article/view/328 |
| _version_ | 1797337517505118208 |
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| author | Muhamad Fawwaz Akbar Muhammad Itqan Mazdadi Muliadi Triando Hamonangan Saragih Friska Abadi |
| author_facet | Muhamad Fawwaz Akbar Muhammad Itqan Mazdadi Muliadi Triando Hamonangan Saragih Friska Abadi |
| author_sort | Muhamad Fawwaz Akbar |
| collection | DOAJ |
| description | In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks. |
| first_indexed | 2024-03-08T09:11:48Z |
| format | Article |
| id | doaj.art-e94590600da441908bde6c7e0d828eec |
| institution | Directory Open Access Journal |
| issn | 2656-8632 |
| language | English |
| last_indexed | 2024-03-08T09:11:48Z |
| publishDate | 2023-09-01 |
| publisher | Poltekkes Kemenkes Surabaya |
| record_format | Article |
| series | Journal of Electronics, Electromedical Engineering, and Medical Informatics |
| spelling | doaj.art-e94590600da441908bde6c7e0d828eec2024-01-31T23:55:01ZengPoltekkes Kemenkes SurabayaJournal of Electronics, Electromedical Engineering, and Medical Informatics2656-86322023-09-015426127010.35882/jeeemi.v5i4.328328Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector MachineMuhamad Fawwaz Akbar0Muhammad Itqan Mazdadi1Muliadi2Triando Hamonangan Saragih3Friska Abadi4Computer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, IndonesiaComputer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, IndonesiaComputer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, IndonesiaComputer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, IndonesiaComputer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, IndonesiaIn the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.https://jeeemi.org/index.php/jeeemi/article/view/328covid-19 vaccineinformation gain ratioparticle swarm optimizationsupport vector machine |
| spellingShingle | Muhamad Fawwaz Akbar Muhammad Itqan Mazdadi Muliadi Triando Hamonangan Saragih Friska Abadi Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine Journal of Electronics, Electromedical Engineering, and Medical Informatics covid-19 vaccine information gain ratio particle swarm optimization support vector machine |
| title | Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine |
| title_full | Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine |
| title_fullStr | Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine |
| title_full_unstemmed | Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine |
| title_short | Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine |
| title_sort | implementation of information gain ratio and particle swarm optimization in the sentiment analysis classification of covid 19 vaccine using support vector machine |
| topic | covid-19 vaccine information gain ratio particle swarm optimization support vector machine |
| url | https://jeeemi.org/index.php/jeeemi/article/view/328 |
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