Bagheri, A, Saraee, M and de Jong, F 2013, Latent dirichlet markov allocation for sentiment analysis , in: The Fifth European Conference on Intelligent Management Systems in Operations (IMSIO 5), Wednesday 03-04 July 2013, Thinklab, University of Salford.
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In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model.
|Item Type:||Conference or Workshop Item (Paper)|
|Themes:||Memory, Text and Place|
|Schools:||Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)|
|Publisher:||The OR Society , Birmingham, UK|
|Funders:||Non funded research|
|Depositing User:||Dr Mo Saraee|
|Date Deposited:||06 Sep 2013 13:52|
|Last Modified:||29 Oct 2015 00:10|
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