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Extended Twofold-LDA Model for Two Aspects in One Sentence

Burns, Nicola, Bi, Yaxin, Wang, Hui and Anderson, Terry (2012) Extended Twofold-LDA Model for Two Aspects in One Sentence. In: Advances in Computational Intelligence Communications in Computer and Information Science. Springer Berlin Heidelberg, pp. 265-275. ISBN 978-3-642-31714-9 [Book section]

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Abstract

The Latent Dirichlet Allocation (LDA) model has been recently used as a method of identifying latent aspects in customer reviews. In our previous work, we proposed Twofold-LDA to identify both aspects and positive or negative sentiment in review sentences. We incorporated domain knowledge (i.e. seed words) to produce more focused aspects and provided a user-friendly chart quantifying sentiment. Our previous work made an assumption that one sentence contains one aspect, but in this study we wish to extend our model to remove this assumption. Experimental results show that our extended model improves the performance for every aspect in the datasets. We also show the importance of seed words for identifying desired aspects.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25452
Deposited By: Dr Yaxin Bi
Deposited On:19 Jan 2016 09:34
Last Modified:19 Jan 2016 09:34

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