Lifelong learning for cross-domain vietnamese sentiment classification

Abstract

This paper proposes an improvement to lifelong learning for cross-domain sentiment classification. Lifelong learning is to retain knowledge from past learning tasks to improve the learning task on a new domain. In this paper, we will discuss how bigram and bag-of-bigram features integrated into a lifelong learning system can help improve the performance of sentiment classification on both Vietnamese and English. Also, pre-processing techniques specifically for our cross-domain, Vietnamese dataset will be discussed. Experimental results show that our method achieves improvements over prior systems and its potential for cross-domain sentiment classification.