Chinese spam classification based on weighted distributed characteristic
Xianlun Tang, Yali Wan, Yuwei Liu, Jun Cai · 2017
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based features deeply represented by Word2vec model. First, TF-IDF is utilized to calculate the weight of characteristic words in text so that the distributed text-based feature representation can be effectively enhanced. Then on the base of these, support vector machine (SVM) algorithm is applied to establish the classification model. The experimental results demonstrate that the proposed approach can not only better represent the text vector, but also significantly improve the recognition accuracy of Chinese spam.