Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers

Intania Cahya Sari; Yova Ruldeviyani

doi:10.1109/IWBIS50925.2020.9255531

Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers

Intania Cahya Sari
, Yova Ruldeviyani

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Citations (Scopus)

Abstract

The appearance of the Covid-19 virus in early 2020 became a frightening pandemic for the world, including Indonesia. The infection of the Covid-19 virus was rapid because of its transmission can be through human contact. This condition causes worrying in society. Besides, these worrying also occurs in the passenger of public transportation, especially the commuter line. Passengers in large numbers and push each other will cause worry if commuter line passengers will transmit the Covid-19 virus to the commuter line. Many passengers write their opinions about the transmission of the Covid-19 pandemic on social media Twitter. This causes various opinions that can be positive, negative, or even neutral. Therefore, to see the opinions on commuter line passengers, a research was made to analyze the sentiment of the Covid-19 transmission to commuter line passengers. This research was implemented using a comparison of 2 methods, Naïve Bayes outperformed the Decision Tree with an accuracy of 73.59%. Furthermore, the result of sentiment analysis was a positive classification compared to the other 2 classes.

Original language	English
Title of host publication	2020 International Workshop on Big Data and Information Security, IWBIS 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	23-28
Number of pages	6
ISBN (Electronic)	9781728190983
DOIs	https://doi.org/10.1109/IWBIS50925.2020.9255531
Publication status	Published - 17 Oct 2020
Event	5th International Workshop on Big Data and Information Security, IWBIS 2020 - Depok, Indonesia Duration: 17 Oct 2020 → 18 Oct 2020

Publication series

Name	2020 International Workshop on Big Data and Information Security, IWBIS 2020

Conference

Conference	5th International Workshop on Big Data and Information Security, IWBIS 2020
Country/Territory	Indonesia
City	Depok
Period	17/10/20 → 18/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Keywords

Covid-19
Decision Tree
Naïve Bayes
Sentiment Analysis

Access to Document

10.1109/IWBIS50925.2020.9255531

Cite this

Sari, I. C., & Ruldeviyani, Y. (2020). Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers. In 2020 International Workshop on Big Data and Information Security, IWBIS 2020 (pp. 23-28). Article 9255531 (2020 International Workshop on Big Data and Information Security, IWBIS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWBIS50925.2020.9255531

Sari, Intania Cahya ; Ruldeviyani, Yova. / Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data : A Case Study of Commuter Line Passengers. 2020 International Workshop on Big Data and Information Security, IWBIS 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 23-28 (2020 International Workshop on Big Data and Information Security, IWBIS 2020).

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title = "Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers",

abstract = "The appearance of the Covid-19 virus in early 2020 became a frightening pandemic for the world, including Indonesia. The infection of the Covid-19 virus was rapid because of its transmission can be through human contact. This condition causes worrying in society. Besides, these worrying also occurs in the passenger of public transportation, especially the commuter line. Passengers in large numbers and push each other will cause worry if commuter line passengers will transmit the Covid-19 virus to the commuter line. Many passengers write their opinions about the transmission of the Covid-19 pandemic on social media Twitter. This causes various opinions that can be positive, negative, or even neutral. Therefore, to see the opinions on commuter line passengers, a research was made to analyze the sentiment of the Covid-19 transmission to commuter line passengers. This research was implemented using a comparison of 2 methods, Na{\"i}ve Bayes outperformed the Decision Tree with an accuracy of 73.59\%. Furthermore, the result of sentiment analysis was a positive classification compared to the other 2 classes. ",

keywords = "Covid-19, Decision Tree, Na{\"i}ve Bayes, Sentiment Analysis",

author = "Sari, \{Intania Cahya\} and Yova Ruldeviyani",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 5th International Workshop on Big Data and Information Security, IWBIS 2020 ; Conference date: 17-10-2020 Through 18-10-2020",

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Sari, IC & Ruldeviyani, Y 2020, Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers. in 2020 International Workshop on Big Data and Information Security, IWBIS 2020., 9255531, 2020 International Workshop on Big Data and Information Security, IWBIS 2020, Institute of Electrical and Electronics Engineers Inc., pp. 23-28, 5th International Workshop on Big Data and Information Security, IWBIS 2020, Depok, Indonesia, 17/10/20. https://doi.org/10.1109/IWBIS50925.2020.9255531

Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers. / Sari, Intania Cahya; Ruldeviyani, Yova.
2020 International Workshop on Big Data and Information Security, IWBIS 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 23-28 9255531 (2020 International Workshop on Big Data and Information Security, IWBIS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The appearance of the Covid-19 virus in early 2020 became a frightening pandemic for the world, including Indonesia. The infection of the Covid-19 virus was rapid because of its transmission can be through human contact. This condition causes worrying in society. Besides, these worrying also occurs in the passenger of public transportation, especially the commuter line. Passengers in large numbers and push each other will cause worry if commuter line passengers will transmit the Covid-19 virus to the commuter line. Many passengers write their opinions about the transmission of the Covid-19 pandemic on social media Twitter. This causes various opinions that can be positive, negative, or even neutral. Therefore, to see the opinions on commuter line passengers, a research was made to analyze the sentiment of the Covid-19 transmission to commuter line passengers. This research was implemented using a comparison of 2 methods, Naïve Bayes outperformed the Decision Tree with an accuracy of 73.59%. Furthermore, the result of sentiment analysis was a positive classification compared to the other 2 classes.

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ER -

Sari IC, Ruldeviyani Y. Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers. In 2020 International Workshop on Big Data and Information Security, IWBIS 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 23-28. 9255531. (2020 International Workshop on Big Data and Information Security, IWBIS 2020). doi: 10.1109/IWBIS50925.2020.9255531

Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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