Automatic classification system and automatic classification method for Web comment viewpoint on the basis of association rule

Abstract

The invention discloses an automatic classification system and an automatic classification method for a Web comment viewpoint on the basis of an association rule. The automatic classification system can be divided into four modules, i.e. a frequent feature word extraction module, a frequent feature word optimization module, an association rule extracting and mining module and a viewpoint classification module. According to the invention, the defects that the traditional system (such as systems based on machine learning and sentiment classification) has low precision or needs mass manpower participation or excessively depends on natural language processing and professional knowledge can be overcome. Optimization is carried out in the association rule set extraction process, redundant association rules with low distinction degree are removed, and therefore, the operation efficiency and the result acquisition efficiency of the whole system can be improved. According to the invention, a precise and convenient solution is provided for various situations, such as E-business commodity evaluation, E-government affair feedback and netizen opinion survey.

Description

technical field [0001] The invention relates to an automatic classification system and classification method of Web comments based on association rules, and belongs to the technical field of semantic processing. Background technique [0002] Traditional text opinion classification methods include opinion classification based on machine learning and opinion classification based on sentiment analysis. [0003] Machine learning-based methods use machine learning algorithms in text classification directly for opinion classification, and the accuracy of opinion classification tasks is usually lower than that of text classification tasks for other categories of topics. The reason is that opinion texts on the Web involve the expression of human emotions, and are text content with a very special theme, and their semantic obscurity is higher than that of objective descriptive texts. There are positive words that express irony, and the opposite also exists. These special patterns are...

AI Technical Summary

Problems solved by technology

The reason is that opinion texts on the Web involve the expression of human emotions, and are text content with a very special theme, and their semantic obscurity is higher than that of objective descriptive texts. There are positive words that express irony, and the opposite also exists. These special patterns are difficult to judge by statistical learning methods

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