Research Article
Review Sentiment Analysis of World Class Hotel Using Naive Bayes Classifier And Particle Swarm Optimization Method
@INPROCEEDINGS{10.4108/eai.24-10-2018.2280546, author={Sopian Aji and Warjiyono Warjiyono and Dany Pratmanto and Angga Ardiansyah and Andrian Eko Widodo and Husni Faqih and Suleman Suleman and Fandhilah Fandhilah}, title={Review Sentiment Analysis of World Class Hotel Using Naive Bayes Classifier And Particle Swarm Optimization Method}, proceedings={The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus}, publisher={EAI}, proceedings_a={ICCSET}, year={2018}, month={11}, keywords={sentiment analysis hotel review naive bayes classifier paticle swarm optimization}, doi={10.4108/eai.24-10-2018.2280546} }- Sopian Aji
Warjiyono Warjiyono
Dany Pratmanto
Angga Ardiansyah
Andrian Eko Widodo
Husni Faqih
Suleman Suleman
Fandhilah Fandhilah
Year: 2018
Review Sentiment Analysis of World Class Hotel Using Naive Bayes Classifier And Particle Swarm Optimization Method
ICCSET
EAI
DOI: 10.4108/eai.24-10-2018.2280546
Abstract
Hotel service website is currently growing very rapidly, along with the development of tourism worldwide. The progress of world-class tourism is influenced by the provision of the international class accommodation. Selecting hotels considers generally expensive facilities, services and other supporting infrastructure. Currently the hotel booking service providers already provide facilities for tourists to write reviews and experiences of staying in the hotel room for other travel recommendations. With so many reviews displayed, it is necessary to perform a classification analysis of the review into a positive or negative grade. The method used for sentiment analysis of the hotel review is Naive Bayes Algorithm and Particle Swarm Optimization, This research examined data from sentiment hotel reviews on several hotel booking websites of 100 positive reviews and 100 negative reviews. The resulting combining Naive Bayes with Particle Swarm Optimization and obtains the best value with accuracy of 85.00%
