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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.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1145/2184436.2184437
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Abstract

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 Article
Copyright Holders: 2012 ACM
ISSN: 1558-3430
Keywords: sentiment analysis; latent Dirichlet allocation; generalized expectation; weakly-supervised sentiment classification
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 31502
Depositing User: Yulan He
Date Deposited: 25 Jan 2012 15:01
Last Modified: 03 Jul 2014 22:38
URI: http://oro.open.ac.uk/id/eprint/31502
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