Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14143
Title: Sentiment classification of online Cantonese reviews by supervised machine learning approaches
Authors: Zhang, Z
Ye, Q
Li, Y
Law, R 
Keywords: Cantonese
SVM classifiers
Machine learning
Online reviews
Sentiment classification
Supervised learning
Support vector machines
Text mining
Issue Date: 2009
Publisher: Inderscience Publishers
Source: International Journal of web engineering and technology, 2009, v. 5, no. 4, p. 382-397 How to cite?
Journal: International Journal of web engineering and technology 
Abstract: Cantonese is an important Chinese dialect spoken in some regions of Southern China. Local online users often represent their opinions and experiences with written Cantonese on the web. With two supervised machine learning approaches, this paper conducts a series of experiments to explore appropriate methods for automatic sentiment classification in the very noisy domain of online Cantonese-written reviews. Findings indicate that the support vector machine classifier based on a Mandarin Chinese word segmentation tool performs surprisingly well. The accuracy, precision and recall respectively for positive and negative reviews all reach above 85% when the training corpus contains 5,000 or more reviews.
URI: http://hdl.handle.net/10397/14143
ISSN: 1476-1289
DOI: 10.1504/IJWET.2009.032254
Appears in Collections:Journal/Magazine Article

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