Abstract
To help customers, especially the customers without explicit purchasing motivation, to obtain valuable information of products via E-commerce websites, it is useful to predict sales rankings of the products. This paper focuses on this problem by finding relationship between reviews, star level and sales rankings of products. We combine various factors with the information of helpfulness and conducting correlation analysis between sales rankings and our combinations to find the most correlative combinations, namely the optimal combinations. We use three domains of books from Amazon.cn to conduct experiments. The main findings show that helpfulness is really useful to predict book sales rankings. Different domains of books have different optimal combinations. In addition, in consideration of helpfulness, the combination of number of positive reviews, score of review stars and score of frequent aspects is the most correlative combination. In this paper, although reviews on Amazon.cn are written in Chinese, our method is language independent.
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Zhou, Q., Zhang, C. (2014). Relationship between Reviews Polarities, Helpfulness, Stars and Sales Rankings of Products: A Case Study in Books. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_16
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DOI: https://doi.org/10.1007/978-3-662-45558-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45557-9
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