Sentiment labeling for extending initial labeled data to improve semi-supervised sentiment classification
作者：
Highlights：
• Semi-supervised framework which exploits unsupervised approach (JST) is proposed.
• Self-training suffers from incorrectly labeling problem with insufficient data.
• Confidently predicted instances are labeled and used as training data by JST.
• Self-training with extended training corpus incrementally improves classifier model.
• Sentiment labeling improves accuracy by enriching initial classifier of self-training.
摘要
•Semi-supervised framework which exploits unsupervised approach (JST) is proposed.•Self-training suffers from incorrectly labeling problem with insufficient data.•Confidently predicted instances are labeled and used as training data by JST.•Self-training with extended training corpus incrementally improves classifier model.•Sentiment labeling improves accuracy by enriching initial classifier of self-training.
论文关键词：Concatenated vector,Paragraph vector,Self-training,Sentiment classification,Sentiment labeling,Topic model
论文评审过程：Received 15 December 2016, Revised 19 September 2017, Accepted 19 September 2017, Available online 20 September 2017, Version of Record 3 October 2017.
论文官网地址：https://doi.org/10.1016/j.elerap.2017.09.006
