Dubai Restaurants: A Sentiment Analysis of Tourist Reviews


  • Vinaitheerthan Renganathan
  • Amitabh Upadhya


An enormous amount of information is available on innumerable travel websites, social media and blogs, of which a large part is user-generated content. This web content holds great potential to assess visitor sentiment at a destination; as this identifies a need for building automated systems to extract unknown sentiments from these sources. Sentiment analysis, which includes text mining and natural language processing (nlp) techniques, helps in extracting related sentiments from the data thus stored, in unstructured formats. The extracted sentiment would facilitate better tourist decision making and improve customer service and new product development for tourism enterprises. This study presents a sentiment analysis model to extract the hidden sentiments from tourist reviews about restaurants in Dubai that will guide visitors to the city in taking suitable dining decisions. Sentiment analysis is carried out by extracting tourist reviews about restaurants in Dubai using a web scraping method using text mining techniques with the help of the R statistical software package. The resultant data is further analysed by sentiment analysis tools to extract the hidden sentiments, which are categorized under eight heads. The sentiment analysis helped uncover hidden sentiments along with the frequency of each sentiment category. It also helped to find the difference between tourist sentiment scores with respect to different categories of restaurants. The paper provides a sentiment analysis model which can be used in the future to extract the reviews related to other tourism products besides restaurants, such as accommodation, attractions and accessibility.

Keywords: tourist reviews, Dubai restaurants, sentiment analysis, text mining, R statistical package




How to Cite

Renganathan, V., & Upadhya, A. (2022). Dubai Restaurants: A Sentiment Analysis of Tourist Reviews. Academica Turistica - Tourism and Innovation Journal, 14(2). Retrieved from