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A Lifelong Sentiment Classification Framework Based on a Close Domain Lifelong Topic Modeling Method

  • Thi-Cham Nguyen
  • Thi-Ngan PhamEmail author
  • Minh-Chau Nguyen
  • Tri-Thanh Nguyen
  • Quang-Thuy Ha
Conference paper
  • 327 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)

Abstract

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.

Keywords

Close domain Lifelong topic modeling Close topic 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Vietnam National University, Hanoi (VNU), VNU-University of Engineering and Technology (UET)HanoiVietnam
  2. 2.Hai Phong University of Medicine and PharmacyHai PhongVietnam
  3. 3.The Vietnamese People’s Police AcademyHanoiVietnam
  4. 4.Japan Advanced Institution of Science and TechnologyNomiJapan

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