Abstract:

In the current era of big data, the Internet blog, forum produce a flood of subjective comment information which express various peoples’ color emotion and emotional tendency. It is so difficult to classify and process the massive comment information only by using the artificial methods, then how to efficiently dig out a lot of information that has appraisive views on the network has become an urgent problem at present. The research on Chinese text appraisive classification technology is the way to solve this problem. This article describes the common text feature selection algorithms, analyzes the shortcomings of document frequency and mutual information algorithm. By comparing and analyzing the two algorithms, combined with the relevance of text feature and text classification and the probability that the text feature appears, this article proposes an improved text feature selection algorithm(MIDF). The experimental results show that, MIDF is valid to the appraisive classification research.

Key words: appraisive classification, text feature selection, appraisive feature extracting