文献类型：期刊文献

英文题名：User OCEAN Personality Model Construction Method Using a BP Neural Network

作者：Qin, Xiaomei[1];Liu, Zhixin[2];Liu, Yuwei[3];Liu, Shan[3];Yang, Bo[3];Yin, Lirong[4];Liu, Mingzhe[5];Zheng, Wenfeng[3]

机构：[1]Xian Fanyi Univ, Coll Translat Studies, Xian 710105, Peoples R China;[2]Shaoxing Univ, Sch Life Sci, Shaoxing 312000, Peoples R China;[3]Univ Elect Sci & Technol China, Sch Automat, Chengdu 610054, Peoples R China;[4]Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70803 USA;[5]Wenzhou Univ Technol, Sch Data Sci & Artificial Intelligence, Wenzhou 325000, Peoples R China

年份：2022

卷号：11

期号：19

外文期刊名：ELECTRONICS

收录：SCI-EXPANDED(收录号：WOS:000866786600001)、、Scopus(收录号：2-s2.0-85139872381)、WOS

基金：This research was funded by the Sichuan Science and Technology Program, grant number 2021YFQ0003. "The innovation and practice of translation teaching model under 'new liberal art + information technology' (No. 2021ND0605)" and "Reform and practice of applied talent cultivation mode of liberal arts majors in non-government universities under the background of new liberal arts (21BZ080)".

语种：英文

外文关键词：OCEAN personality model; digital footprint; LDA topic model; neural network

外文摘要：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.