Multi-domain Sentiment Classification

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).

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Shoushan Li, Chengqing Zong
• Keywords: ACL 2008, Computational Linguistics, Multi-domain Sentiment Classification, Multiple Domains, Training Data