@inproceedings{77f99c65511a4b9fb9d8d95510ea2adb,
title = "Improving opinion aspect extraction using semantic similarity and aspect associations",
abstract = "Aspect extraction is a key task of fine-grained opinion mining. Although it has been studied by many researchers, it remains to be highly challenging. This paper proposes a novel unsupervised approach to make a major improvement. The approach is based on the framework of lifelong learning and is implemented with two forms of recommendations that are based on semantic similarity and aspect associations respectively. Experimental results using eight review datasets show the effectiveness of the proposed approach.",
author = "Qian Liu and Bing Liu and Yuanlin Zhang and Kim, {Doo Soon} and Zhiqiang Gao",
note = "Funding Information: Bing Liu's research was partially supported by the NSF grants IIS-1111092 and IIS-1407927, and a gift award from Bosch. Yuanlin Zhang's work was partially supported by the NSF grants IIS-1018031 and CNS-1359359. Zhiqiang Gao's research was partially supported by the 863 program grant 2015AA015406 and the NSF of China grant 61170165. Publisher Copyright: {\textcopyright} Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; null ; Conference date: 12-02-2016 Through 17-02-2016",
year = "2016",
language = "English",
series = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",
publisher = "AAAI press",
pages = "2986--2992",
booktitle = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",
}