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Incorporating sentiment prior knowledge for weakly-supervised sentiment analysis

He, Yulan (2012). Incorporating sentiment prior knowledge for weakly-supervised sentiment analysis. ACM Transactions on Asian Language Information Processing, 11(2), article no. 4.

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This paper presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly-supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDA-GE perform com- parably to existing weakly-supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering them more suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.

Item Type: Journal Item
Copyright Holders: 2012 ACM
ISSN: 1558-3430
Keywords: sentiment analysis; latent Dirichlet allocation; generalized expectation; weakly-supervised sentiment classification
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 31502
Depositing User: Yulan He
Date Deposited: 25 Jan 2012 15:01
Last Modified: 08 Dec 2018 20:28
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