Abstract
In this paper we propose a probabilistic graph model to estimate the importance weights of product attributes from customer reviews. In this model, each product aspect has two weights: one weight indicates its importance level when customers’ opinions about the product are generally positive; the other one indicates its importance level when the opinions are negative. Those weights provide on-line retailers with insight into the advantages and disadvantages of their products and allow them to devise effective methods to increase on-line sales.
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Acknowledgement
This paper is partly supported by The Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2014.22.
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Ou, W., Le, AC., Huynh, VN. (2016). Estimating Asymmetric Product Attribute Weights in Review Mining. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_21
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DOI: https://doi.org/10.1007/978-3-319-49046-5_21
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