[1]QIN Haifei,DU Junping.Feature mining based on online hotel review[J].CAAI Transactions on Intelligent Systems,2018,13(6):1006-1014.[doi:10.11992/tis.201806016]
                                
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                                    CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
                                    13
                                    Number of periods:
                                    2018 6
                                    Page number:
                                    1006-1014
                                    Column:
                                    学术论文—人工智能基础
                                    Public date:
                                    2018-10-25
                                
                                
                                    - Title:
- 
                                        Feature mining based on online hotel review
                                    - Author(s):
- 
                                        QIN Haifei1;  DU Junping2
- 
                                        1. School of Information Science and Technology, Chuxiong Normal University, Chuxiong 675000, China;
 2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
- 
                                
                                    - Keywords:
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                                        hotel; online review; data capture; feature extract; feature mining; cluster analysis; classification; intelligent recommendation
                                    - CLC:
- 
                                        TP391
                                    - DOI:
- 
                                        10.11992/tis.201806016
                                    - Abstract:
- 
                                        In this study, the feature mining of online hotel review data is investigated. First, online hotel reviews data were obtained. To mine features from the review data, data cleaning, part-of-speech analysis, feature extraction, index determination, feature selection, feature determination, feature checking were carried out. Based on the word frequency, integrating part-of-speech analysis, and cluster analysis, the word frequency (TF), word frequency rate (TF1), word frequency weight (TTW), comment frequency (DF), inverse document frequency (IDF), and TF1-IDF of candidate feature words were applied to reduce dimension. The online hotel review data features were obtained, and then the feature words were verified. This paper will lay a solid foundation for the classification of hotels and customers and intelligent recommendation based on online reviews.