| Issue |
E3S Web Conf.
Volume 53, 2018
2018 3rd International Conference on Advances in Energy and Environment Research (ICAEER 2018)
|
|
|---|---|---|
| Article Number | 03039 | |
| Number of page(s) | 4 | |
| Section | Environment Engineering, Environmental Safety and Detection | |
| DOI | https://doi.org/10.1051/e3sconf/20185303039 | |
| Published online | 14 September 2018 | |
Research on Credit Evaluation Model of Online Store Based on SnowNLP
1
Department of E-commerce, School of Information Science and Technology, Hainnan Normal University, Haikou 571158,P.R.China.
2
School of economic management, Hainan College of Vocation and Technique, Haikou 570105, P. R. China ;
* Corresponding author: Caixia Chen:914922505@qq.com
The online store credit rating is a reflection of the seller's integrity and the quality of the product. The level of the credit rating directly affects the buyer's desire to purchase. Two important factors affecting the credit rating are data and models. The innovation of this research is that the collected data comes from the second evaluation, and the credit evaluation model is improved based on the snowNLP tool, and the malicious brushing filtering function is added. Compared with the credit evaluation system commonly used in current online stores, the evaluation results of the paper are more accurate, detailed and intuitive, and may effectively reduce false brushing and threat review.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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