Sentiment Analysis for Hotel Reviews Using Snowball and VADER

Abstract

In the hotel industry, the role of hotel guests is very influential in the development and sustainability of business. Therefore, hotels need to provide services that can satisfy guests. However, many hotels still do not have an analysis system for guest comments. Hotels still manually conduct analysis by discussing with operational leaders to determine whether incoming guest comments contain positive, negative, or neutral sentiments. Previous research introduced guest sentiment analysis but has yet to have optimal accuracy. This paper proposes sentiment analysis using a combination of VADER and Snowball stemmer algorithms, which are tested using real datasets. The goal is to get accurate sentiment analysis results. The experimental results show that the VADER method combined with SnowBall Stemmer has better accuracy than other sentiment analysis methods, with an accuracy of 96.21%. The sentiment analysis model can be used as a basis for decision-making for hotel business owners.