Semi-supervised text sentiment classification method based on random feature subspace

Abstract

The invention discloses a semi-supervised text sentiment classification method based on random feature subspace. The method comprises steps as follows: 1 acquiring and pre-processing comment text data and constructing a global feature set; 2 expressing all comment texts in a vector form; 3 marking part of comment texts and acquiring a marked sample set and an unmarked sample set; 4 calculating feature weight of all feature words in the global feature set; 5 constructing random subspace; 6 performing cooperative training with unmarked samples to obtain Z classifiers finally; 7 integrating the Z classifiers in a main voting manner to obtain a final integrated classifier. The method solves the problems that a large number of misclassified samples exist in the training process with traditional cooperative training algorithms and the difference between classifiers is small in semi-supervised text sentiment classification methods, so that the accuracy of the text sentiment classification method is improved.

Description

technical field [0001] The invention belongs to the field of natural language processing technology and pattern recognition, in particular to a semi-supervised text emotion classification method based on random feature subspace. Background technique [0002] In recent years, with the rapid development of the Internet, more and more Internet users are willing to publish their opinions and comments on the Internet, resulting in a large number of subjective texts created by users. Such subjective texts contain emotional information such as user views, opinions, and attitudes. Therefore, analyzing the emotional information expressed in subjective texts and identifying their emotional tendencies plays an important role for Internet users. Analyzing text sentiment requires a large number of labeled samples, but in practical applications, it is quite easy to collect a large number of unlabeled samples, and it takes a lot of manpower and material resources to mark these unlabeled sa...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiencies in the prior art, the present invention proposes a semi-supervised text sentiment classification method based on random subspace, in order to solve the problem of a large number of misclassified samples in the training process of the traditional cooperative training algorithm and the semi-supervised text sentiment classification method. In the classification method, the difference between the base classifiers is small, so as to further improve the accuracy of the text sentiment classification method

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