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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dellarocas, C., Zhang, X.M., Awad, N.F.: Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing 21(4), 23–45 (2007)
Yin, D., Bond, S.D., Zhang, H.: Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. Mis Quarterly 38(2), 539–560 (2014)
Liu, J., Cao, Y., Lin, C.Y., et al.: Low-Quality Product Review Detection in Opinion Summarization. In: Proceedings of EMNLP-CoNLL, pp. 334–342 (2007)
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Mullen, T., Collier, N.: Sentiment Analysis using Support Vector Machines with Diverse Information Sources. In: Proceedings of EMNLP, pp. 412–418 (2004)
Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences 181(6), 1138–1152 (2011)
Li, S., Lee, S.Y.M., Chen, Y., Huang, C.-R., Zhou, G.: Sentiment classification and polarity shifting. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 635–643 (2010)
Turney, P.D.: Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424 (2002)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Computational Linguistics 37(2), 267–307 (2011)
Cambria, E., Hussain, A., Havasi, C., Eckl, C., Munro, J.: Towards crowd validation of the UK national health service. In: Proceedings of WebSci, pp. 1–5 (2010)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Data Mining, pp. 231–240 (2008)
Moghaddam, S., Ester, M.: On the design of LDA models for aspect-based opinion mining. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 803–812 (2012)
Chang, P.C., Lai, C.Y.: A hybrid system combining self-organizing maps with case-based reasoning in wholesaler’s new-release book forecasting. Expert Systems with Applications 29(1), 183–192 (2005)
Tanaka, K.: A sales forecasting model for new-released and nonlinear sales trend products. Expert Systems with Applications 37(11), 7387–7393 (2010)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)
Lee, W.I., Chen, C.W., Chen, K.H., et al.: A comparative study on the forecast of fresh food sales using logistic regression, moving average and BPNN methods. Journal of Marine Science and Technology 20(2), 142–152 (2012)
Yu, X., Liu, Y., Huang, X., et al.: Mining online reviews for predicting sales performance: A case study in the movie domain. IEEE Transactions on Knowledge and Data Engineering 24(4), 720–734 (2012)
Hongzhan, N., Lü Pan, Q.Y., Yao, X.: Comprehensive fuzzy evaluation for transmission network planning scheme based on entropy weight method. Power System Technology 33(11), 60–64 (2009)
Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications, A State of the Art Survey. Springer, New York (1981)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-662-45558-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45557-9
Online ISBN: 978-3-662-45558-6
eBook Packages: Computer ScienceComputer Science (R0)