Twitter Sentiment Analysis in Tourism with Polynomial Naïve Bayes Classifier-Reference-Cited by-同舟云学术

Twitter Sentiment Analysis in Tourism with Polynomial Naïve Bayes Classifier

Published:2024-02-01 Issue:4 Volume:5 Page:343-353
ISSN:2684-9151
Container-title:JTIM : Jurnal Teknologi Informasi dan Multimedia
language:
Short-container-title:jtim

Author:

Rizal Ahmad Ashril^ORCID,Nugraha Gibran Satya,Putra Rian Asmara,Anggraeni Dara Puspita

Abstract

Lombok has become a favorited tourist destination in the world. Therefore, tourism is a mainstay sector in regional development in West Nusa Tenggara. The contribution of the tourism sector shows an increasing trend. Tourist expenditures are distributed to various sectors. The tourism sector has a positive impact on the regional economy. The local government has prepared to improve the quality and quantity of tourism in Lombok. The results of local government efforts need to be analyzed so that future policies are on target. Analysis can be done on the satisfaction of tourists who travel to Lombok. It would be very difficult to get satisfaction data from all tourists through questionnaires. But on the other hand, tourist satisfaction is usually posted on their social networks. One of the social media that is widely used by tourists is Twitter. Their tweets contain not only expressions of natural beauty but also criticism, suggestions, and complaints during their visit. In addition, the tweet data on twitter is open access. This study tries to analyze the sentiment on Twitter which contains tweets of tourists who have visited Lombok. Sentiment analysis is performed using the Polynomial Naive Bayes Classifier. Sentiment results are classified into positive and negative sentiments. The results of this sentiment are expected to help related agencies or other tourism actors to improve the quality and quantity of regional tourism. The results showed that the positive sentiment on the security factor were 50.65%, the cost 75.32%, accommodation 62.33% and the cleanness factor 77.92%.

Publisher

Sekawan Institute

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