The Open UniversitySkip to content
 

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

He, Yulan; Lin, Chenghua and Alani, Harith (2011). Automatically extracting polarity-bearing topics for cross-domain sentiment classification. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 19 - 24 Jun 2011, Portland, Oregon, USA.

Full text available as:
[img]
Preview
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (387Kb)
URL: http://acl2011.org/accepted_papers.shtml
Google Scholar: Look up in Google Scholar

Abstract

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.

Item Type: Conference Item
Copyright Holders: 2011 The Authors
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetNot SetEC-FP7 projects ROBUST [257859]
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Centre for Policing Research and Learning (CPRL)
Item ID: 28546
Depositing User: Kay Dave
Date Deposited: 18 Apr 2011 08:45
Last Modified: 04 Oct 2016 18:21
URI: http://oro.open.ac.uk/id/eprint/28546
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

▼ Automated document suggestions from open access sources

Actions (login may be required)

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk