A Lifelong Sentiment Classification Framework Based On A Close Domain Lifelong Topic Modeling Method

In lifelong machine learning, the determination of the hypotheses related to the current task is very meaningful thanks to the reduction of the space to look for the knowledge patterns supporting for solving the current task. However, there are few studies for this problem. In this paper, we propose the definitions for measuring the "close domains to the current domain", and a lifelong sentiment classificationmethod based on using the close domains for topic modeling the current domain. Experimental results on sentiment datasets of product reviews from Amazon.com show the promising performance of system and the effectiveness of our approach.