Federated Learning-Inspired User Personality Prediction Using Sentiment Analysis and Topic Preference

• Published in: IEEE transactions on consumer electronics Vol. 70; no. 1; pp. 2729 - 2737
• Main Authors: Zhao, Jinghua, Zhao, Jiale, Chi, Chenxin, Wang, Chengyin, Zhao, Xiaohua, Jain, Amar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blogs; Commutation; Convolutional neural networks; Data mining; Data models; Dictionaries; Dirichlet problem; Federated learning; LDA; LSTM; Personality; personality prediction; Prediction models; Predictive control; Predictive models; Preferences; Sentiment analysis; Social networking (online); The big five
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2023.3324453

Users are accustomed to posting their opinions on social platforms, and these text data can be used to analyze users' preferences and personality traits. To predict the personality type of users, a user personality prediction model based on sentiment analysis and theme preference is proposed. This work employs latent Dirichlet allocation (LDA) to extract different topics of user comments and long-short term memory (LSTM) for text sentiment analysis. Then, the Big Five personality theory is introduced and combined with the text sentiment analysis method. Based on the data of the social event "28 years of commutation life", the experimental results show that the accuracy of the sentiment classification on the validation set is 92%. Combined with the Big Five personality theory, the largest proportion of users' emotions is "anger", accounting for 23%. There are more users with extroverted personalities and neurotic personalities. These conclusions provide information in regard to predicting and controlling the direction of public opinion.