Abstract
In the cosmetics domain, many online sellers support user-provided product reviews. It has been shown that reviews have a profound effect on product conversion rates. Reviews of cosmetic products carry particular importance in purchasing decisions because of their personal nature, and particularly because of the potential for irritation with unsuitable products. In this paper, we propose a method for automatic scoring of various aspects of cosmetic item review texts based on a curated dictionary of expressions from a corpus of real world online reviews. Results and discussion of a user experiment to evaluate the approach are presented. In particular, we find that a co-occurrence approach improved coverage of reviews, and that our automated approach predicted attributes in manually annotated ground truth with an accuracy of 79%.
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Acknowledgements
This work was supported in part by the MEXT Grant-in Aid for Scientific Research(C)(#16K00425, #26330351).
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Matsunami, Y., Ueda, M., Nakajima, S., Hashikami, T., O’Donovan, J., Kang, B. (2017). Mining Attribute-Specific Ratings from Reviews of Cosmetic Products. In: Ao, SI., Kim, H., Huang, X., Castillo, O. (eds) Transactions on Engineering Technologies. IMECS 2016. Springer, Singapore. https://doi.org/10.1007/978-981-10-3950-8_8
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DOI: https://doi.org/10.1007/978-981-10-3950-8_8
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