Twitter Sentiment Analysis on the Implementation of Online Learning during the Pandemic using Naive Bayes and Support Vector Machine

I. Wayan Suasnawa, I. Gusti Ngurah Bagus Caturbawa, I. Gede Suputra Widharma, Anak Agung Ngurah Gde Sapteka, I. Indrayana, I. Sunaya

2021

Abstract

The Covid-19 pandemic situation presents a new phenomenon in the world of education. In this situation, it is not possible to conduct face-to-face learning so that online learning becomes the main choice. The online learning method certainly has advantages and disadvantages. There are many comments, both pros and cons regarding the implementation of this online learning. People’s sentiments can be grouped into three, those who feel that the implementation of online learning is able to provide a good solution (positive), those who consider it not an effective solution (negative), and those that are not both (neutral). In this study, the data used in the social media Twitter. In this study, the Naive Bayes classifier and the Support Vector Machine will be used to obtain sentiment analysis on the implementation of online learning during the pandemic. The results of this study indicate that public sentiment is classified into three classes positive, negative and neutral with a precision level of 0.76 (positive), 0.79 (negative) and 0.92 (neutral) in machine learning using the Naïve Bayes classifier and 0.78 (positive), 0.50 (negative). ) and 0.54 (neutral) on machine learning using the Support Vector Machine classifier. Meanwhile, the accuracy value is above 0.8 for the Naïve Bayes classifier and 0.61 for the Support Vector Machine classifier. The results obtained in machine learning with 2 different classifiers show that the Naïve Bayes classifier has better precision and accuracy than the Support Vector Machine.

Paper Citation

in Harvard Style

Wayan Suasnawa I., Gusti Ngurah Bagus Caturbawa I., Gede Suputra Widharma I., Agung Ngurah Gde Sapteka A., Indrayana I. and Sunaya I. (2021). Twitter Sentiment Analysis on the Implementation of Online Learning during the Pandemic using Naive Bayes and Support Vector Machine. In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES, ISBN 978-989-758-615-6, pages 348-353. DOI: 10.5220/0010945500003260

in Bibtex Style

@conference{icast-es21,
author={I. Wayan Suasnawa and I. Gusti Ngurah Bagus Caturbawa and I. Gede Suputra Widharma and Anak Agung Ngurah Gde Sapteka and I. Indrayana and I. Sunaya},
title={Twitter Sentiment Analysis on the Implementation of Online Learning during the Pandemic using Naive Bayes and Support Vector Machine},
booktitle={Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES,},
year={2021},
pages={348-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010945500003260},
isbn={978-989-758-615-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES,
TI - Twitter Sentiment Analysis on the Implementation of Online Learning during the Pandemic using Naive Bayes and Support Vector Machine
SN - 978-989-758-615-6
AU - Wayan Suasnawa I.
AU - Gusti Ngurah Bagus Caturbawa I.
AU - Gede Suputra Widharma I.
AU - Agung Ngurah Gde Sapteka A.
AU - Indrayana I.
AU - Sunaya I.
PY - 2021
SP - 348
EP - 353
DO - 10.5220/0010945500003260