Cross-domain polarity classification using a knowledge-enhanced meta-classifier
作者：
Highlights：
• We propose a new generic meta-learning-based approach to polarity categorization.
• Study impact of word sense disambiguation and vocabulary expansion-based features.
• State-of-the-art results on single and cross-domain polarity categorization.
• Our approach does not perform any domain adaptation, therefore it is generic.
• Our approach obtains the most stable results across the different tested domains.
摘要
•We propose a new generic meta-learning-based approach to polarity categorization.•Study impact of word sense disambiguation and vocabulary expansion-based features.•State-of-the-art results on single and cross-domain polarity categorization.•Our approach does not perform any domain adaptation, therefore it is generic.•Our approach obtains the most stable results across the different tested domains.
论文关键词：Sentiment analysis,Cross-domain polarity classification,Meta-learning,Word sense disambiguation,Semantic network
论文评审过程：Received 30 September 2014, Revised 15 May 2015, Accepted 18 May 2015, Available online 23 May 2015, Version of Record 31 July 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.05.020
