User OCEAN Personality Model Construction Method Using a BP Neural Network
In the era of big data, the Internet is enmeshed in people’s lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/19/3022 |
| _version_ | 1797479864989646848 |
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| author | Xiaomei Qin Zhixin Liu Yuwei Liu Shan Liu Bo Yang Lirong Yin Mingzhe Liu Wenfeng Zheng |
| author_facet | Xiaomei Qin Zhixin Liu Yuwei Liu Shan Liu Bo Yang Lirong Yin Mingzhe Liu Wenfeng Zheng |
| author_sort | Xiaomei Qin |
| collection | DOAJ |
| description | In the era of big data, the Internet is enmeshed in people’s lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to the present research, user’s behavior on social networking sites has a great correlation with their personality, and the five characteristics of the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model can cover all aspects of a user’s personality. It is important in identifying a user’s OCEAN personality model to analyze their digital footprints left on social networking sites and to extract the rules of users’ behavior, and then to make predictions about user behavior. In this paper, the Latent Dirichlet Allocation (LDA) topic model is first used to extract the user’s text features. Second, the extracted features are used as sample input for a BP neural network. The results of the user’s OCEAN personality model obtained by a questionnaire are used as sample output for a BP neural network. Finally, the neural network is trained. A mapping model between the probability of the user’s text topic and their OCEAN personality model is established to predict the latter. The results show that the present approach improves the efficiency and accuracy of such a prediction. |
| first_indexed | 2024-03-09T21:51:56Z |
| format | Article |
| id | doaj.art-7c49a9b1f3c944c7820504b41225a32d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:51:56Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-7c49a9b1f3c944c7820504b41225a32d2023-11-23T20:04:58ZengMDPI AGElectronics2079-92922022-09-011119302210.3390/electronics11193022User OCEAN Personality Model Construction Method Using a BP Neural NetworkXiaomei Qin0Zhixin Liu1Yuwei Liu2Shan Liu3Bo Yang4Lirong Yin5Mingzhe Liu6Wenfeng Zheng7College of Translation Studies, Xi’an Fanyi University, Xi’an 710105, ChinaSchool of Life Science, Shaoxing University, Shaoxing 312000, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USASchool of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaIn the era of big data, the Internet is enmeshed in people’s lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to the present research, user’s behavior on social networking sites has a great correlation with their personality, and the five characteristics of the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model can cover all aspects of a user’s personality. It is important in identifying a user’s OCEAN personality model to analyze their digital footprints left on social networking sites and to extract the rules of users’ behavior, and then to make predictions about user behavior. In this paper, the Latent Dirichlet Allocation (LDA) topic model is first used to extract the user’s text features. Second, the extracted features are used as sample input for a BP neural network. The results of the user’s OCEAN personality model obtained by a questionnaire are used as sample output for a BP neural network. Finally, the neural network is trained. A mapping model between the probability of the user’s text topic and their OCEAN personality model is established to predict the latter. The results show that the present approach improves the efficiency and accuracy of such a prediction.https://www.mdpi.com/2079-9292/11/19/3022OCEAN personality modeldigital footprintLDA topic modelneural network |
| spellingShingle | Xiaomei Qin Zhixin Liu Yuwei Liu Shan Liu Bo Yang Lirong Yin Mingzhe Liu Wenfeng Zheng User OCEAN Personality Model Construction Method Using a BP Neural Network Electronics OCEAN personality model digital footprint LDA topic model neural network |
| title | User OCEAN Personality Model Construction Method Using a BP Neural Network |
| title_full | User OCEAN Personality Model Construction Method Using a BP Neural Network |
| title_fullStr | User OCEAN Personality Model Construction Method Using a BP Neural Network |
| title_full_unstemmed | User OCEAN Personality Model Construction Method Using a BP Neural Network |
| title_short | User OCEAN Personality Model Construction Method Using a BP Neural Network |
| title_sort | user ocean personality model construction method using a bp neural network |
| topic | OCEAN personality model digital footprint LDA topic model neural network |
| url | https://www.mdpi.com/2079-9292/11/19/3022 |
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