Sentiment Classification for Online Comments Based on Random Network Theory
Peng Qin · Acta Automatica Sinica · 2010
We propose a new method of sentiment classification named SCP-X(shortest covering path-X) for online comment based on the random network theory.A new approach which is proved to be effiective by experiments is presented to create the word co-occurrenced network incrementally.With the network,the sequences of online comments,which are shorter,more optional and more fragmentary,are extended by shortest covering path(SCP) proposed in this paper.Using this algorithm,the amount of attributes is reduced to about 10% compared to VSM.Finally,experiments are designed to compare the results of the algorithms such as TC,SVM,SCP-TC,SCP-SVM,SCP-HMM,and SCP-Bayes.The results indicate that SCP-X is remarkably effective to classify online comments by sentiment orientation.