A Survey of Sentiment Analysis in Halal Tourism Using Machine Learning and Lexicon Approaches for Extracting Sentiment Polarity

Halal tourism is a rapidly growing industry that caters to the needs of Muslim travelers worldwide. This field’s research is also becoming more intriguing since it offers products such as halal restaurants, sharia hotels, banking, medicines, and finance. With the increasing popularity of halal tourism, it is essential to understand the sentiments of tourists regarding halal tourism products and services. Sentiment analysis is a useful tool to extract the polarity of reviews and opinions about products or services. In this paper, we present a survey of existing literature on sentiment analysis in halal tourism and the use of machine learning techniques or extracting sentiment polarity from reviews. We discuss various machine learning algorithms, including Random Forest, Naive Bayes, Support Vector Machine, Decision Tree, Convolutional Neural Network, BERT, and attention-based models, used for sentiment analysis in halal tourism. Our survey also covers various aspects of halal tourism, including food, cosmetics, hotels, and finance sectors. This paper highlights the importance of sentiment analysis in halal tourism and provides insight for future research.