Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model
A BERT-MDLP-Bayesian Network model (BMB) is proposed to analyze the improvement strategy of e-commerce products based on user generated content (UGC). The proposed model can be represented into four parts: clearing redundant data on the obtained UGC, extracting product attributes and word vector to...
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| Format: | Article |
| Language: | English |
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Springer
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822001021 |
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| _version_ | 1849324829827661824 |
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| author | Ming-Fang Li Guo-Xiang Zhang Lu-Tao Zhao Tao Song |
| author_facet | Ming-Fang Li Guo-Xiang Zhang Lu-Tao Zhao Tao Song |
| author_sort | Ming-Fang Li |
| collection | DOAJ |
| description | A BERT-MDLP-Bayesian Network model (BMB) is proposed to analyze the improvement strategy of e-commerce products based on user generated content (UGC). The proposed model can be represented into four parts: clearing redundant data on the obtained UGC, extracting product attributes and word vector to generate product attributes, establishing product attribute Bayesian network corresponding to UGC, and inferring the causal relationship between product attributes. In order to verify the effectiveness of the proposed model, an amazon tablet product is used for empirical analysis. Compared with the traditional model, BMB model has better performance in product feature mining in three aspects of feature diversity, feature long tail and attribute difference. In application, the model can effectively describe the core problems of products, and provide suggestions for e-commerce to modify marketing strategies and determine the new direction of product development. |
| format | Article |
| id | doaj-art-fd0b7e841bb34aabaa7b7fe28ac632b1 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-fd0b7e841bb34aabaa7b7fe28ac632b12025-08-20T03:48:35ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-06-013462720273210.1016/j.jksuci.2022.03.018Extracting product competitiveness through user-generated content: A hybrid probabilistic inference modelMing-Fang Li0Guo-Xiang Zhang1Lu-Tao Zhao2Tao Song3School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China; Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China; School of Management and Economics, Beijing Institute of Technology, Beijing, China; Corresponding author at: School of Management and Economics, Beijing Institute of Technology, Beijing, China.School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China; Cheil PengTai Company Limited, Beijing, ChinaA BERT-MDLP-Bayesian Network model (BMB) is proposed to analyze the improvement strategy of e-commerce products based on user generated content (UGC). The proposed model can be represented into four parts: clearing redundant data on the obtained UGC, extracting product attributes and word vector to generate product attributes, establishing product attribute Bayesian network corresponding to UGC, and inferring the causal relationship between product attributes. In order to verify the effectiveness of the proposed model, an amazon tablet product is used for empirical analysis. Compared with the traditional model, BMB model has better performance in product feature mining in three aspects of feature diversity, feature long tail and attribute difference. In application, the model can effectively describe the core problems of products, and provide suggestions for e-commerce to modify marketing strategies and determine the new direction of product development.http://www.sciencedirect.com/science/article/pii/S1319157822001021Text miningBayesian networkSocial mediaSentiment analysisUser-generated content |
| spellingShingle | Ming-Fang Li Guo-Xiang Zhang Lu-Tao Zhao Tao Song Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model Journal of King Saud University: Computer and Information Sciences Text mining Bayesian network Social media Sentiment analysis User-generated content |
| title | Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model |
| title_full | Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model |
| title_fullStr | Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model |
| title_full_unstemmed | Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model |
| title_short | Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model |
| title_sort | extracting product competitiveness through user generated content a hybrid probabilistic inference model |
| topic | Text mining Bayesian network Social media Sentiment analysis User-generated content |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822001021 |
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