@inproceedings{SisLab1461,
       booktitle = {NICS: 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science},
           month = {September},
           title = {A Least Square based Model for Rating Aspects and Identifying Important Aspects on Review Text Data},
          author = {Duc Hong Pham and Anh Cuong Le and Thi Kim Chung Le},
            year = {2015},
           pages = {265--270},
        keywords = {data mining;least squares approximations;natural language processing;text analysis;cleanliness;hotel services;knowledge mining;least square based model;location;natural language processing;opinion mining;rating product aspects;review text data;room;sentiment analysis;service;value;Algorithm design and analysis;Computer science;Dictionaries;Hidden Markov models;Mathematical model;Prediction algorithms;Training},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1461/},
        abstract = {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.}
}