Online Review Analysis Using Density-Based Spatial Clustering of Applications with Noise

Abstract

This study applies a density-based clustering method to analyze user perceptions based on reviews on Google Maps. The focus of this research lies in processing dynamic, unlabeled review data to address managers' needs in understanding public sentiment. A total of 399 data sets were collected through Apify, then the data were processed through cleaning, normalization, and stemming stages. Text representation was performed by weighting word frequencies across documents, while WordCloud visualization was utilized to identify dominant words reflecting positive perceptions to help understand the context before the clustering process. The Density-Based Spatial Clustering of Applications with Noise method was applied to form review clusters. The analysis results show that this method is able to group reviews into clusters based on content similarity, although some data were identified as noise. These findings provide useful insights in understanding public perception, thus aiding in strategic decision-making. With the right parameter selection, this method can be an effective approach for further public review sentiment analysis.