TY  - CONF
ID  - SisLab4043
UR  - http://dx.doi.org/10.1007/978-3-030-41964-6_50
A1  - Nguyen, Thi Cham
A1  - Pham, Thi Ngan
A1  - Nguyen, Minh Chau
A1  - Nguyen, Tri Thanh
A1  - Ha, Quang Thuy
Y1  - 2020/03/04/
N2  - 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 classification method 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.
VL  - 12033
SN  - 0302-9743
TI  - A Lifelong Sentiment Classification Framework Based on a Close Domain Lifelong Topic Modeling Method
SP  - 575
M2  - Phuket, Thailand
AV  - none
EP  - 585
T2  - Asian Conference on Intelligent Information and Database Systems
ER  -