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

Autor:	Tri-Thanh Nguyen, Thi-Ngan Pham, Quang-Thuy Ha, Thi-Cham Nguyen, Minh-Chau Nguyen
Rok vydání:	2020
Předmět:	Topic model business.industry Computer science Space (commercial competition) Machine learning computer.software_genre Domain (software engineering) Task (project management) Reduction (complexity) Product reviews Classification methods Artificial intelligence business computer
Zdroj:	Intelligent Information and Database Systems ISBN: 9783030419639 ACIIDS (1)
DOI:	10.1007/978-3-030-41964-6_50
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3412e9df93fc5485a769a3b560d70901 https://doi.org/10.1007/978-3-030-41964-6_50 Zobrazit plný text záznamu