Automatic domain ontology extraction for context-sensitive opinion mining

Lau, Raymond Y.K., Lai, Chapmann C.L., Ma, Jian, & Li, Yuefeng (2009) Automatic domain ontology extraction for context-sensitive opinion mining. In ICIS 2009 Proceedings, AIS Electronic Library, Phoenix, Arizona, pp. 35-53.

View at publisher


Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from, our system shows remarkable performance improvement over the context-free baseline.

Impact and interest:

12 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

585 since deposited on 22 Jun 2011
52 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 42065
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: consumer feedbacks, business strategy, opinion mining, fuzzy domain ontology
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2009 [please consult the authors]
Deposited On: 22 Jun 2011 03:06
Last Modified: 11 Aug 2011 05:22

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page