| Issue |
E3S Web Conf.
Volume 464, 2023
The 2nd International Conference on Disaster Mitigation and Management (2nd ICDMM 2023)
|
|
|---|---|---|
| Article Number | 06002 | |
| Number of page(s) | 7 | |
| Section | Public Health Risk, Including Covid-19 | |
| DOI | https://doi.org/10.1051/e3sconf/202346406002 | |
| Published online | 18 December 2023 | |
Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic
1 Faculty of Computing and Informatics, Universiti Malaysia Sabah, Sabah, Malaysia
2 Faculty of Pharmacy Science and Technology, Information System Department, Universitas Dharma Andalas, Padang, Indonesia
3 Informatics Engineering Department, Institut Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia
4 Civil Engineering Department, Universitas Andalas, Padang, Indonesia
* Corresponding author: laipohung@ums.edu.my
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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