Sentiment labeling for extending initial labeled data to improve semi-supervised sentiment classification

In recent decades, analyzing the sentiments in online customer reviews has become important to many businesses and researchers. However, insufficient amount of labeled training corpus is a bottleneck for machine learning approaches. Self-training is one of the promising semi-supervised techniques which does not require large amounts of labeled data. However, self-training also suffers from an incorrect labeling problem along with insufficient amount of labeled data. This study proposed a semi-supervised learning framework that adds only confidently predicted data to the training corpus in order to enrich the initial classifier in self-training. The experimental results indicate that the proposed method performed better than self-training.