An Empirical Study on Multi-dimensional Sentiment Analysis from User Service Reviews

Abstract

Online reviews on a service are important sources for service providers to improve their service delivery and service consumers to obtain information for decision making before their service acquisition. However, in the real situation, there are several points of view (dimensions) in service assessment using online reviews. This paper shows an empirical study to apply classification-based sentiment analysis on online reviews with multiple dimensions using natural language processing techniques. The aim of this study is to find the most influential part-of-speech on the sentimental analysis and the performance of the multi-dimensional classification methods. By the experiments on reviews of restaurants with five dimensions, i.e., taste, environment, service, price, and cleanness, we find out that adjective (JJ) has the most influential part-of-speech on the sentimental analysis and BRplus is the most efficient one with the classification accuracy of 85.89%.