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.
Bibliographical noteFunding Information:
This work (Grant No. C0258734) was supported by the Business for Academic-industrial Cooperative Establishments funded by the Korea Small and Medium Business Administration in 2016. This work was also financially supported by the Korea Ministry of Land, Infrastructure and Transport ( MOLIT ) via the U-City Master and Doctor Course Grant Program.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
- Management of Technology and Innovation