Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs
Zhiguang Liu, Xishuang Dong, Yi Guan, Jinfeng Yang · 2013
The imbalanced sentiment distribution of microblogs induces bad performance of binary classifiers on the minority class. To address this problem, we present a semi-supervised method for sentiment classi-fication of Chinese microblogs. This method is similar to self-training, except that, a set of labeled samples is reserved for a confidence scores computing pro-cess through which samples that are less than a predefined confidence score thresh-old are incorporated into training set for retraining. By doing this, the classifier is able to boost the performance on the mi-nority class samples. Experiments on the NLP&CC2012 Chinese microblog evalu-ation data set demonstrated that reserved self-training outperforms the best run by 2.06 % macro-averaged and 2.30 % micro-averaged F-measure, respectively. 1