Proceedings of the International Conference on Applied Science and Technology on Social Science 2022 (iCAST-SS 2022)

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Comparison of Support Vector Machine and Random Forest Algorithms in Sentiment Analysis on Covid-19 Vaccination on Twitter Using Vader and Textblob Labelling

Authors
Berliana Putri Meliani1, *, Oktariani Nurul Pratiwi1, Rachmadita Andreswari1
1Information System Department, Telkom University, Bandung, Indonesia
*Corresponding author. Email: berlianaputrii@student.telkomuniversity.ac.id
Corresponding Author
Berliana Putri Meliani
Available Online 30 December 2022.
DOI
10.2991/978-2-494069-83-1_108How to use a DOI?
Keywords
support vector machine; random forest; vadersentiment; textblob; confusion matrix
Abstract

Corona Virus Disease 2019 is a world outbreak that was first reported in Wuhan in December 2019. The first case of Covid-19 in Indonesia was confirmed on March 2, 2020. Covid-19 is caused by infection with virus named SARS-Cov-2. Currently, social media is widely used to find out public opinion. Generally, on Twitter social media, issues that are currently hot and much discussed by the public will become Trending Conversations. To find out and filter the opinions on social media, whether they include positive or negative opinions, you can use Sentiment Analysis. In this study, the sentiment analysis about covid-19 vaccination will use the Support Vector Machine (SVM) and Random Forest algorithms. The dataset will be labeled using the VaderSentiment and Textblob libraries found in Python. This comparison results that the SVM algorithm with textblob labeling produces an accuracy of 0.8940. Meanwhile, the sentiment results show that people tend to have negative opinions. Therefore, the best modeling for sentiment analysis is to use the Support Vector Machine with Textblob labeling.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applied Science and Technology on Social Science 2022 (iCAST-SS 2022)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
30 December 2022
ISBN
978-2-494069-83-1
ISSN
2352-5398
DOI
10.2991/978-2-494069-83-1_108How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Berliana Putri Meliani
AU  - Oktariani Nurul Pratiwi
AU  - Rachmadita Andreswari
PY  - 2022
DA  - 2022/12/30
TI  - Comparison of Support Vector Machine and Random Forest Algorithms in Sentiment Analysis on Covid-19 Vaccination on Twitter Using Vader and Textblob Labelling
BT  - Proceedings of the International Conference on Applied Science and Technology on Social Science 2022 (iCAST-SS 2022)
PB  - Atlantis Press
SP  - 620
EP  - 626
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-83-1_108
DO  - 10.2991/978-2-494069-83-1_108
ID  - Meliani2022
ER  -