Latent dirichlet markov allocation for sentiment analysis

Bagheri, A; Saraee, MH; de Jong, F

Abstract

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.

Citation

Bagheri, A., Saraee, M., & de Jong, F. Latent dirichlet markov allocation for sentiment analysis. Presented at The Fifth European Conference on Intelligent Management Systems in Operations (IMSIO 5), Thinklab, University of Salford

• Presentation Conference Type: Other
• Conference Name: The Fifth European Conference on Intelligent Management Systems in Operations (IMSIO 5)
• Conference Location: Thinklab, University of Salford
• Publication Date: Jul 3, 2013
• Deposit Date: Sep 6, 2013
• Publicly Available Date: Sep 6, 2013
• Related Public URLs: http://www.theorsociety.com/Pages/Conferences/IMSIO5/IMSIO5.aspx
• Additional Information: Event Type : Conference