Application of locally linear embedding algorithm on hotel data text classification - Huang, Jinming (Journal of physics. Conference series, 2020)
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.