Sentiment Analysis On Covid-19 Outbreak Awareness Using Naïve Bayes Algorithm

Sabri, Norlina Mohd; Norman, Jasmine Nor Azman; Isa, Naimah Mat; Bahrin, Ummu Fatihah Mohd

doi:10.1109/ivit55443.2022.10033379

Cited by 1 publication

(1 citation statement)

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“…Tourism is now slowly recovering and is back again as one of the country"s sources of economic growth. The outbreak of the Covid-19 virus is not the biggest concern nowadays since people can get treatment if infected [3].…”

Section: Introductionmentioning

confidence: 99%

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

Sabri,

Subki,

Bahrin

et al. 2024

IJACSA

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The tourism industry is one of the hard hit businesses during the Covid-19 pandemic and has been struggling for backup ever since. However, nowadays the industry has started to bloom again with the lifting of all of the restrictions of Covid-19. This research aims to analyze the sentiments of the tourists using the Support Vector Machine (SVM) algorithm to know their views on the tourist spots after the pandemic. The scope of the research covers the state of Terengganu which is popularly known for its islands and unique culture on the east coast of Malaysia. TikTok data has been used as the source of data as social media currently has become one of the top mediums for reviewing, selling and promoting products and services. The objective of the research is to explore the SVM algorithm in the sentiment classification of tourist spots in Terengganu. This research is expected to help the Tourism Terengganu to improve their tourist spots and their services. The phases of the research include collecting data from TikTok, data pre-processing, data labelling, feature extraction, model creation using SVM, graphical user interface development and performance evaluation. The evaluation results showed that the performance of the SVM classifier model was good and reliable, with 90.68% accuracy. The future work would be collecting more data from TikTok regularly to further improve the accuracy of the algorithm.

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Section: Introductionmentioning

confidence: 99%