A least square based model for rating aspects and identifying important aspects on review text data
Duc-Hong Pham, Anh-Cuong Le, Thi-Kim-Chung Le · 2015
Opinion mining and sentiment analysis has been one of the attracting topics of knowledge mining and natural language processing in recent years. The problem of rating aspects from textual reviews is an important task in this field. In this paper we propose a new method for rating product aspects as well as for identifying important aspects in general. Our proposed model is based on the least square method. The experiments are carried out on the data collected from hotel services with the aspects including the cleanliness, location, service, room, and value. We have obtained more accurate results than some well-known previous studies.