User OCEAN Personality Model Construction Method Using a BP Neural Network

• Published in: Electronics (Basel) Vol. 11; no. 19; p. 3022
• Main Authors: Qin, Xiaomei, Liu, Zhixin, Liu, Yuwei, Liu, Shan, Yang, Bo, Yin, Lirong, Liu, Mingzhe, Zheng, Wenfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Accuracy; Algorithms; Analysis; Back propagation networks; Big Data; Cyberbullying; Dirichlet problem; Feature extraction; Footprints; Internet; Linguistics; Machine learning; Model accuracy; Natural language; Neural networks; Personality; Personality traits; Psychologists; Social network analysis; Social networks; Social research; Text analysis; User behavior; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11193022

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.