A Novel Text Classification Method Using Comprehensive Feature Weight
Jian Liu, Weisheng Wu
Currently, since the categorical distribution of short text corpus is not balanced, it is difficult to obtain accurate classification results for long text classification. To solve this problem, this paper proposes a novel method of short text classification using comprehensive feature weights. This method takes into account the situation of the samples in the positive and negative categories, as well as the category correlation of words, so as to improve the existing feature weight calculation method and obtain a new method of calculating the comprehensive feature weight. The experimental result shows that the proposed method is significantly higher than other feature-weight methods in the micro and macro average value, which shows that this method can greatly improve the accuracy and recall rate of short text classification.
text classification; text categorization; comprehensive feature weight; feature selection