Dynamic partition mechanism in sample space for sentiment recognition of Chinese texts
Lu Tie · Jisuanji yingyong yanjiu · 2013
To improve the performance of sentiment recognition for Chinese texts,this paper proposed a dynamic partition mechanism based on sample space to build the sentiment classifier in terms of ensemble learning method.Firstly,it utilized a kernel smoothing method to adaptively divide the sample space into several multi-granularity sample subsets.Then,it trained a base classifier on each subset by a learning algorithm.Finally,it combined the outputs from all base classifiers to form a final recognition result.To evaluate the method,the experiment was conducted on a Chinese benchmark dataset.The results indicate that the method is better than Bagging and AdaBoost algorithm in both precision and recall rate.Furthermore,it has a good application prospect in Chinese sentiment recognition for plenty of samples.