Method of Text Sentiment Classification Based on Pseudo Relevance Feedback
Lin Lu · Jisuanji fangzhen · 2013
In the process of machine learning,it is necessary to build incremental model with automatic learning capabilities. For incremental model based on Pseudo- relevance feedback,the research on how to improve the confidence of feedback samples is still important,although some feedback strategy had been given. This paper presented a pseudo relevance feedback method based on K- Means clustering. For documents classified by Naive Bayesian classifier,we searched the center vector by means of reducing the sample number gradually,and extracted feedback samples and feature concentration using for improve the performance of NB classifier. We carried out experiments in Chinese text sentiment classification according to the pseudo relevance feedback strategy. This method converts the posterior probability into prior probability in a degree. The results show that with the expansion of feature concentration,the strategy can achieve better than baseline in precision and recall.