Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection
Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Theref...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/11/7/155 |
| _version_ | 1828743508800831488 |
|---|---|
| author | Yufeng Wang Shuangrong Liu Songqian Li Jidong Duan Zhihao Hou Jia Yu Kun Ma |
| author_facet | Yufeng Wang Shuangrong Liu Songqian Li Jidong Duan Zhihao Hou Jia Yu Kun Ma |
| author_sort | Yufeng Wang |
| collection | DOAJ |
| description | Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition. |
| first_indexed | 2024-04-13T01:39:38Z |
| format | Article |
| id | doaj.art-725f0cf4911a4fccab2f7acb5335aeed |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-04-13T01:39:38Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-725f0cf4911a4fccab2f7acb5335aeed2022-12-22T03:08:14ZengMDPI AGFuture Internet1999-59032019-07-0111715510.3390/fi11070155fi11070155Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention DetectionYufeng Wang0Shuangrong Liu1Songqian Li2Jidong Duan3Zhihao Hou4Jia Yu5Kun Ma6School of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSocial network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.https://www.mdpi.com/1999-5903/11/7/155marketing intentionfeature extractionensemble learning |
| spellingShingle | Yufeng Wang Shuangrong Liu Songqian Li Jidong Duan Zhihao Hou Jia Yu Kun Ma Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection Future Internet marketing intention feature extraction ensemble learning |
| title | Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection |
| title_full | Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection |
| title_fullStr | Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection |
| title_full_unstemmed | Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection |
| title_short | Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection |
| title_sort | stacking based ensemble learning of self media data for marketing intention detection |
| topic | marketing intention feature extraction ensemble learning |
| url | https://www.mdpi.com/1999-5903/11/7/155 |
| work_keys_str_mv | AT yufengwang stackingbasedensemblelearningofselfmediadataformarketingintentiondetection AT shuangrongliu stackingbasedensemblelearningofselfmediadataformarketingintentiondetection AT songqianli stackingbasedensemblelearningofselfmediadataformarketingintentiondetection AT jidongduan stackingbasedensemblelearningofselfmediadataformarketingintentiondetection AT zhihaohou stackingbasedensemblelearningofselfmediadataformarketingintentiondetection AT jiayu stackingbasedensemblelearningofselfmediadataformarketingintentiondetection AT kunma stackingbasedensemblelearningofselfmediadataformarketingintentiondetection |