Abstract
In this paper, we establish a model to analysis business enterprise customer query information for text classification to help e-commerce companies control the user’s spending habits, and help users to find their needed goods. This study accesses to customer inquiry data and preprocesses these text data firstly. Then, it applies the improved TF-IDF principle to obtain the text feature vectors. Finally, this study establishes the classification model combining the Naive Bayes text classification and the semi-supervised EM iterative algorithm and uses various criteria to evaluate the model. When facing multi-class text classification feature selection, keyword weights prone to great volatility. This study improves the keyword weight calculation formula to perfect the classification results. The experimental results show that classification has good classification effect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Feigenbaum, E.A., McCorduck, P.: The Fifth Generation: Artificial Intelligence and Japan’s Challenge to the World. Addison-Wesley, Boston (1983)
Chen, J., Huang, H., Tian, S., et al.: Feature selection for text classification with Naïve Bayes. Expert Syst. Appl. 36(3), 5432–5435 (2009)
Greenshtein, E., Park, J.: Application of non parametric empirical Bayes estimation to high dimensional classification. J. Mach. Learn. Res. 10, 1687–1704 (2009)
Luxburg, U.V.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Nigam, K., Mccallum, A.K., Thrun, S., et al.: Text Classification from labeled and unlabeled documents using EM. Mach. Learn. 39(2–3), 103–134 (2000)
Acknowledgement
Project supported by National Natural Science Foundation of China (61170038, 61472231), Jinan City independent innovation plan project in College and Universities, China (201401202), Ministry of education of Humanities and social science research project, China (12YJA630152), Social Science Fund Project of Shandong Province, China (11CGLJ22), outstanding youth scientist foundation project of Shandong Province, China (BS2013DX037).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sun, W., Xiang, L., Liu, X., Zhao, D. (2016). A Novel Web Text Classification Model Based on SAS for e-commerce. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_100
Download citation
DOI: https://doi.org/10.1007/978-3-319-31854-7_100
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31853-0
Online ISBN: 978-3-319-31854-7
eBook Packages: Computer ScienceComputer Science (R0)