Bilingual sentiment classification method and device

Abstract

The invention provides a bilingual sentiment classification method and device. The method comprises the steps of translating original language documents to be classified and the original language documents of a training sample set to obtain the translated documents to be classified and the translated documents of the training sample set; combining the original language documents to be classified and the translated documents to be classified to obtain a bilingual documents to be classified, and combining the original language documents of the training sample set and the translated documents of the training sample set to obtain bilingual documents of the training sample set; establishing a bilingual feature vector space to be classified and a bilingual feature vector space of the training sample set; training classifiers on the bilingual feature vector space of the training sample set by using a maximum entropy model; carrying out sentiment polarity classification on the bilingual feature vector space to be classified through the trained classifiers. The bilingual sentiment classification method and device combine the characteristics of two languages, provide extra classification information for the sentiment classification, improve classification accuracy, extract important characteristic items from the bilingual feature vector spaces, and improve classification efficiency.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a bilingual emotion classification method and device. Background technique [0002] In recent years, sentiment classification technology has shown great application demands and application prospects in e-commerce, public opinion analysis, information security and other fields. Sentiment classification technology can help understand users' consumption habits and product advantages and disadvantages, and automatically analyze and make decisions on product reviews; understand people's satisfaction and demands, and discover social characteristics in a timely manner; analyze current social hot public opinion information, and provide users, Enterprises, governments, etc. provide important decision-making references. The sentiment classification methods in the prior art are mainly aimed at one language, and only need to be aimed at English. [0003] In the process of re...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the present invention provides a bilingual emotion classification method and device to solve the problem that the emotion classification method in the prior art will cause errors in the classification results and affect the accuracy of classification. The technical solution is as follows:

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