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This paper addresses a new task in sentiment classification, called multi-domain sentiment classification, that aims to improve performance through fusing training data from multiple domains. To achieve this, we propose two approaches of fusion, feature-level and classifier-level, to use training data from multiple domains simultaneously. Experimental studies show that multi-domain sentiment classification using the classifier-level approach performs much better than single domain classification (using the training data individually). . | Multi-domain Sentiment Classification Shoushan Li and Chengqing Zong National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China sshanli cqzong @nlpr.ia.ac.cn Abstract This paper addresses a new task in sentiment classification called multi-domain sentiment classification that aims to improve performance through fusing training data from multiple domains. To achieve this we propose two approaches of fusion feature-level and classifier-level to use training data from multiple domains simultaneously. Experimental studies show that multi-domain sentiment classification using the classifier-level approach performs much better than single domain classification using the training data individually . 1 Introduction Sentiment classification is a special task of text categorization that aims to classify documents according to their opinion of or sentiment toward a given subject e.g. if an opinion is supported or not Pang et al. 2002 . This task has created a considerable interest due to its wide applications. Sentiment classification is a very domainspecific problem training a classifier using the data from one domain may fail when testing against data from another. As a result real application systems usually require some labeled data from multiple domains guaranteeing an acceptable performance for different domains. However each domain has a very limited amount of training data due to the fact that creating large-scale high-quality labeled corpora is difficult and time-consuming. Given the limited multi-domain training data an interesting task arises how to best make full use of all training data to improve sentiment classification performance. We name this new task multi-domain sentiment classification . In this paper we propose two approaches to multi-domain sentiment classification. In the first called feature-level fusion we combine the feature sets from all the domains into one feature set. Using the unified .
