He, Yulan; Alani, Harith and Zhou, Deyu
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This paper presents a weakly-supervised method for Chinese sentiment analysis by incorporating lexical prior knowledge obtained from English sentiment lexicons through machine translation. A mechanism is introduced to incorporate the prior information about polarity bearing words obtained from existing sentiment lexicons into latent Dirichlet allocation (LDA) where sentiment labels are considered as topics. Experiments on Chinese product reviews on mobile phones, digital cameras, MP3 players, and monitors demonstrate the feasibility and effectiveness of the proposed approach and show that the weakly supervised LDA model performs as well as supervised classifiers such as Naive Bayes and Support vector Machines with an average of 83% accuracy achieved over a total of 5484 review documents. Moreover, the LDA model is able to extract highly domain-salient polarity words from text.
|Item Type:||Conference Item|
|Copyright Holders:||2010 The Authors|
|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)|
|Depositing User:||Yulan He|
|Date Deposited:||07 Dec 2010 10:17|
|Last Modified:||03 Aug 2016 05:25|
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