Multi-domain Adaptation for Sentiment Classification:using Multiple Classifier Combining Methods

摘要：

Sentiment classification is very domain-specific and good domain adaptation methods, when the training and testing data are drawn from different domains, are sorely needed. In this paper, we address a new approach to domain adaptation for sentiment classification in which classifiers are adapted for a specific domain with training data from multiple source domains. We call this new approach ‘multi-domain adaptation’ and present a multiple classifier system (MCS) framework to describe and understand it. Under this framework, we propose a new combining method, called Multi-label Consensus Training (MCT), to combine the base classifiers for selecting ‘automatically-labeled’ samples from unlabeled data in the target domain. The experimental results for sentiment classification show that multi-domain adaptation using this method improves adaptation performance.

关键词： Sentiment classification domain adaptation multiple classifier combining

作者: Shoushan LI Chengqing ZONG

作者单位: National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing,100190

会议类型: 国际会议

会议名称: The 2008 IEEE International Conference on Natural Language Processing and Knowledge Engineering(IEEE NLP-KE 2008)(2008IEEE自然语言处理与知识工程国际会议)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-19（万方平台首次上网日期，不代表论文的发表时间）