Unsupervised classification of sentiment and objectivity in Chinese text

Zagibalov, T and Carroll, John (2008) Unsupervised classification of sentiment and objectivity in Chinese text. In: Proceedings of the Third International Joint Conference on Natural Language Processing (IJCNLP), Hyderabad, India.

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We address the problem of sentiment and
objectivity classification of product reviews
in Chinese. Our approach is distinctive
in that it treats both positive / negative
sentiment and subjectivity / objectivity not
as distinct classes but rather as a continuum;
we argue that this is desirable from
the perspective of would-be customers who
read the reviews. We use novel unsupervised
techniques, including a one-word
'seed' vocabulary and iterative retraining
for sentiment processing, and a criterion of
'sentiment density' for determining the extent
to which a document is opinionated.
The classifier achieves up to 87% F-measure
for sentiment polarity detection.

Item Type: Conference or Workshop Item (Paper)
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: T Technology
Related URLs:
Depositing User: Juan Loera Gonzalez
Date Deposited: 30 Apr 2012 10:21
Last Modified: 30 Apr 2012 10:21
URI: http://srodev.sussex.ac.uk/id/eprint/38707
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