Method and system for carrying out sentiment classification on Chinese comment text on basis of ensemble learning

Abstract

The invention relates to the field of the mode identification, and discloses a method for carrying out sentiment classification on a Chinese comment text on the basis of the ensemble learning and a system on the basis of the method. The method comprises the following steps of: a, acquiring the Chinese comment text from a webpage and carrying out preprocessing on the Chinese comment text; b, sequentially training a multi-classifier system in parallel; c, classifying the comment text to be classified by a base classifier and converting a classification output into an intuitionistic fuzzy number; and d, combining a weight and a guide variable of the base classifier, fusing the sentiment tendency of the comment text to be classified and making a classification decision. The method and the system have the following advantages that the training and classifying speed is ultrahigh; a sequence learning strategy is adopted, so that newly developed vocabularies can be found conveniently and the requirement on a corpus can be lowered; and the classification accuracy is improved by the ensemble learning, and thus, the system on the basis of the method can support the management or purchase decision more satisfactorily.

Description

technical field [0001] The invention is directed at the research on the sentiment classification method of comment text, relates to the field of pattern recognition, and in particular relates to a sentiment classification method of Chinese comment text based on integrated learning and a system based on the method. Background technique [0002] The popularity of the Internet and the emergence of various new network media not only bring massive information to people, but also provide people with various stages to express their emotions, such as online comment platforms such as BLOG, BBS, and news comments. Therefore, how to scientifically and efficiently manage these online comments on the network containing personal emotions is particularly important for the safety of individuals, enterprises, and society. However, these review texts are significantly different from ordinary texts: first, the review text has no fixed grammatical structure, is short in length, and even new wor...

AI Technical Summary

Problems solved by technology

This largely limits the scope of application of the technology

[0006] (2) Although ensemble learning can improve the instability of a single classifier, the traditional ensemble learning method only simply considers the support degree of the classifier to the category of the sample, ignoring the output of the classifier and the fact that the sample is not stable. The degree of belonging to a class, without quantitatively taking into account the fuzzy or probabilistic uncertainty of the classifier output

However, corpus preparation is difficult

More importantly, the one-time trained classifier cannot capture the role of some emerging vocabulary in expressing emotion

Images