Improving opinion aspect extraction using semantic similarity and aspect associations

Qian Liu, Bing Liu, Yuanlin Zhang, Doo Soon Kim, Zhiqiang Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Scopus citations

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.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Pages2986-2992
Number of pages7
ISBN (Electronic)9781577357605
StatePublished - 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
CountryUnited States
CityPhoenix
Period02/12/1602/17/16

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  • Cite this

    Liu, Q., Liu, B., Zhang, Y., Kim, D. S., & Gao, Z. (2016). Improving opinion aspect extraction using semantic similarity and aspect associations. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2986-2992). (30th AAAI Conference on Artificial Intelligence, AAAI 2016). AAAI press.