Latent dirichlet markov allocation for sentiment analysis

Bagheri, A, Saraee, MH ORCID: 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
Publisher: The OR Society , Birmingham, UK
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Prof. Mo Saraee
Date Deposited: 06 Sep 2013 13:52
Last Modified: 28 Aug 2021 04:52

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