• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5905-5926
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• 2017

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.

• Journal of Information Technology Applications and Management
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• v.15 no.3
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• pp.43-59
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• 2008

This paper studies small and medium sized financial agency's management states that take advantage of the Korea Federation of Saving Bank's data. It also presents the management state and the decision-making model that monitors progressive insolvency by standardizing transfer path between relevant groups. With this in mind, we extracted explanatory variables for predictions of insolvency by using existing studies of document related insolvency. First of all, we designed a state model based on demarcated groups to take advantage of the self organizing map that groups in line with a neural network. Secondly, we developed a transition model by standardizing the transfer path between individual banks in a state model. Finally, we presented a decision-making model that integrated the state model and the transition model. This paper will provide groundwork for methods of insolvency prevention to businesses in order for them to have a smooth management system in the financial agencies.