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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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1634; no. 1; pp. 12014 - 12019
Main Author: Huang, Jinming
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.09.2020
Subjects:
Algorithms
Classification
Embedding
Hotels & motels
Machine learning
Manifolds (mathematics)
Physics
Text categorization
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary: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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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1634/1/012014