Application of locally linear embedding algorithm on hotel data text classification
As a non-linear dimension reduction method, manifold learning algorithm projects high-dimensional input to a low-dimensional space by maintaining the local structure of the data, and discovers the inherent geometric structure hidden in the data. In this paper, we attempt to apply the manifold learni...
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| Vydáno v: | Journal of physics. Conference series Ročník 1634; číslo 1; s. 12014 - 12019 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Bristol
IOP Publishing
01.09.2020
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | As a non-linear dimension reduction method, manifold learning algorithm projects high-dimensional input to a low-dimensional space by maintaining the local structure of the data, and discovers the inherent geometric structure hidden in the data. In this paper, we attempt to apply the manifold learning algorithm to the field of Chinese text classification, and use the locally linear embedding algorithm to reduce the dimension of the ctrip hotel review data set. Then, we utilize extreme gradient boosting (XGBoost) and logistic regression to classify the text. Experimental results show that it is effective and feasible to use manifold learning algorithm for text classification. Moreover, the classification effect of logistic regression is better than XGBoost in the text classification of hotel reviews. |
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| AbstractList | As a non-linear dimension reduction method, manifold learning algorithm projects high-dimensional input to a low-dimensional space by maintaining the local structure of the data, and discovers the inherent geometric structure hidden in the data. In this paper, we attempt to apply the manifold learning algorithm to the field of Chinese text classification, and use the locally linear embedding algorithm to reduce the dimension of the ctrip hotel review data set. Then, we utilize extreme gradient boosting (XGBoost) and logistic regression to classify the text. Experimental results show that it is effective and feasible to use manifold learning algorithm for text classification. Moreover, the classification effect of logistic regression is better than XGBoost in the text classification of hotel reviews. |
| Author | Huang, Jinming |
| Author_xml | – sequence: 1 givenname: Jinming surname: Huang fullname: Huang, Jinming email: jinminghhh@163.com organization: College of mathematics and systems science, Shandong university of science and technology , China |
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| Cites_doi | 10.1126/science.290.5500.2319 10.3724/SP.J.1087.2010.02917 10.1126/science.290.5500.2323 |
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| DOI | 10.1088/1742-6596/1634/1/012014 |
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| SubjectTerms | Algorithms Classification Embedding Hotels & motels Machine learning Manifolds (mathematics) Physics Text categorization |
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