He, Yulan
(2012).
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| DOI (Digital Object Identifier) Link: | http://dx.doi.org/doi:10.1145/2184436.2184437 |
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| Google Scholar: | Look up in Google Scholar |
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 |
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| 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: | 07 Feb 2013 15:43 |
| URI: | http://oro.open.ac.uk/id/eprint/31502 |
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