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- Title
User OCEAN Personality Model Construction Method Using a BP Neural Network.
- Authors
Qin, Xiaomei; Liu, Zhixin; Liu, Yuwei; Liu, Shan; Yang, Bo; Yin, Lirong; Liu, Mingzhe; Zheng, Wenfeng
- Abstract
Highlights: What are the main findings? First, the combination of the methods of machine learning with psychological methods to predict the user's OCEAN personality model could achieve a higher accuracy; Second, the model proposed in this paper that is a combination of LDA plus BP neural network is generally superior to the combination model of the same type. What is the implication of the main finding? First, through digital footprints and understanding the rules of user behavior, user behavior can be predicted and targeted recommendations made; Second, it was found that predicting the user's OCEAN personality model and then their behavior can provide an effective method for micro-directional recommendations in network communication. 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.
- Subjects
ONLINE social networks; DIGITAL communications; DIGITAL footprint; PERSONALITY; PSYCHOLOGICAL techniques
- Publication
Electronics (2079-9292), 2022, Vol 11, Issue 19, p3022
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics11193022