Title: Research and analysis of psychological data based on machine learning methods

Authors: Guangshun Chen; Wei Lv; Junwei Ma; Yanchun Liang

Addresses: Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, Jilin, China ' Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, Jilin, China ' Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, Jilin, China ' Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, Jilin, China; Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, Jilin, China

Abstract: The integration of psychology and computer science has become the mainstream contemporary research method on psychological data. Weibo, China's largest open platform for communication and information sharing between users, has many emotional contents hidden in its data. According to the current trend, the Weibo data are segmented by machine learning to obtain a psychological portrait of Weibo users. This design uses Long and Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to perform sentiment classification on Weibo data. The classification results are analysed using word frequency analysis and the Latent Dirichlet Allocation (LDA) model to obtain portraits of Weibo users' sentiment and an analysis of the results. The results are displayed in the form of word clouds. According to the clustering results of the word clouds, the main factors affecting different polar emotions can be analysed.

Keywords: recurrent neural network; short-term memory network; convolutional neural network; emotion analysis; LDA.

DOI: 10.1504/IJWMC.2022.122480

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.1, pp.1 - 8

Received: 20 Aug 2020
Accepted: 16 Dec 2020
Published online: 27 Apr 2022 *

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