Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic

The COVID-19 pandemic has an impact on the economy of Padang City. To revive the economy, especially in the tourism sector, the government is trying to improve services to visitors so that the number of tourist visits increases again. As one of the efforts is to find out the opinion of tourists on t...

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Main Authors: Renita Astri, Lai Po Hung, Suaini Binti Sura, Ahmad Kamal Ariffin Mohd Rus, Rina Yuliet
Format: Proceedings
Language:English
English
Published: EDP Sciences 2023
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/38788/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38788/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38788/
https://www.researchgate.net/publication/376614932_Sentiment_analysis_using_naive_bayes_for_reviews_of_visitors_to_Padang_City_beach_tourism_after_the_COVID-19_pandemic
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Institution: Universiti Malaysia Sabah
Language: English
English
id my.ums.eprints.38788
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spelling my.ums.eprints.387882024-06-10T06:44:32Z https://eprints.ums.edu.my/id/eprint/38788/ Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic Renita Astri Lai Po Hung Suaini Binti Sura Ahmad Kamal Ariffin Mohd Rus Rina Yuliet HD2321-4730.9 Industry T58.4 Managerial control systems The COVID-19 pandemic has an impact on the economy of Padang City. To revive the economy, especially in the tourism sector, the government is trying to improve services to visitors so that the number of tourist visits increases again. As one of the efforts is to find out the opinion of tourists on the beach tourism area visited. This research aims to assist the Padang City government in knowing the positive or negative responses of tourists through the sentiment analysis process to the beach tourism they visit so that The Government of Padang City can determine the policies to be taken in connection with the reviews given by beach tourism visitors. By using reviews on Google Maps on the attractions of Air Manis Beach, Padang Beach, Pasir Jambak Beach, Nirwana Beach, and Pasir Putih Beach, clustering is carried out with the Naive Bayes classification algorithm. Based on the results of the analysis that has been done, 2 of the 5 beaches get negative reviews, namely Pasir Jambak Beach and Pasir Putih Beach which get negative values of 0.550 and 0.650. EDP Sciences 2023 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/38788/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/38788/2/FULL%20TEXT.pdf Renita Astri and Lai Po Hung and Suaini Binti Sura and Ahmad Kamal Ariffin Mohd Rus and Rina Yuliet (2023) Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic. https://www.researchgate.net/publication/376614932_Sentiment_analysis_using_naive_bayes_for_reviews_of_visitors_to_Padang_City_beach_tourism_after_the_COVID-19_pandemic
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic HD2321-4730.9 Industry
T58.4 Managerial control systems
spellingShingle HD2321-4730.9 Industry
T58.4 Managerial control systems
Renita Astri
Lai Po Hung
Suaini Binti Sura
Ahmad Kamal Ariffin Mohd Rus
Rina Yuliet
Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic
description The COVID-19 pandemic has an impact on the economy of Padang City. To revive the economy, especially in the tourism sector, the government is trying to improve services to visitors so that the number of tourist visits increases again. As one of the efforts is to find out the opinion of tourists on the beach tourism area visited. This research aims to assist the Padang City government in knowing the positive or negative responses of tourists through the sentiment analysis process to the beach tourism they visit so that The Government of Padang City can determine the policies to be taken in connection with the reviews given by beach tourism visitors. By using reviews on Google Maps on the attractions of Air Manis Beach, Padang Beach, Pasir Jambak Beach, Nirwana Beach, and Pasir Putih Beach, clustering is carried out with the Naive Bayes classification algorithm. Based on the results of the analysis that has been done, 2 of the 5 beaches get negative reviews, namely Pasir Jambak Beach and Pasir Putih Beach which get negative values of 0.550 and 0.650.
format Proceedings
author Renita Astri
Lai Po Hung
Suaini Binti Sura
Ahmad Kamal Ariffin Mohd Rus
Rina Yuliet
author_facet Renita Astri
Lai Po Hung
Suaini Binti Sura
Ahmad Kamal Ariffin Mohd Rus
Rina Yuliet
author_sort Renita Astri
title Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic
title_short Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic
title_full Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic
title_fullStr Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic
title_full_unstemmed Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic
title_sort sentiment analysis using naive bayes for reviews of visitors to padang city beach tourism after the covid-19 pandemic
publisher EDP Sciences
publishDate 2023
url https://eprints.ums.edu.my/id/eprint/38788/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38788/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38788/
https://www.researchgate.net/publication/376614932_Sentiment_analysis_using_naive_bayes_for_reviews_of_visitors_to_Padang_City_beach_tourism_after_the_COVID-19_pandemic
_version_ 1802978347765989376