Social media data has become a popular source for uncovering hidden patterns in user mobility. However, the volume of online customer reviews has increased enormously, making it difficult for individuals and businesses to identify and analyze these patterns. Moreover, very few studies exist that explore the mobility patterns of reviewers. Therefore, this study aims to profile the mobility of reviewers to identify hidden patterns. The data of 1217 reviewers and 139,187 reviews from Yelp is used in this study. Firstly, distance-based mobility profiles which include the average distance and total distance of reviewers are created. The correlation analysis showed that distance-based measures strongly correlate with the number of reviews written by a reviewer, the number of cities, and the number of states visited by the reviewer compared to other features. Clustering is done using k-means and Density-based spatial clustering of applications with noise (DBSCAN) to analyze the different nature of reviewers which showed that reviewers can be grouped into two categories that are “less traveler” and “more traveler”. A comparison of reviewers who have visited the same number of businesses reveals that their mobility patterns can vary significantly. Finally, the classification of reviewers into less and more travelers is done using various machine learning algorithms. Random forest (RF) achieved Area Under the Curve (AUC) of 0.865, which is comparatively better than other algorithms. The feature importance calculated using RF showed that review count, city, and state are more important features compared to others.