Recent Development of Machine Learning Strategies for Restaurant Review Sentiment Classification: Challenges, Applications, and Opportunities

Abstract

Online restaurant reviews have grown in number significantly providing an excellent source of customer reviews for analyzing quality of the food, services offered, ambiance of the place, pricing policy and other aspects of customers' dining experience. Hence, restaurant review sentiment classification is a good way to classify customer opinions into positive, negative and neutral sentiments automatically. This review paper gives a comprehensive overview of sentiment analysis techniques used for sentiment classification of restaurant customers' reviews. It gives an insight into the history of development of these algorithms from the feature engineering era to modern ML-based approaches. It explores different types of sentiment analysis techniques, features of restaurant reviews, application areas and challenges of using these algorithms. Comparative analysis of existing research papers on this topic helps to understand their advantages, weaknesses and further perspectives. Thus, it can be seen that while traditional methods of sentiment classification are rather computationally effective, their contextual understanding is quite low in contrast to transformer- and language models-based techniques.