Title: Natural language processing-based machine learning psychological emotion analysis method

Authors: Yang Zhao

Addresses: Zhengzhou Shengda University, Zhengzhou 451191, Henan, China

Abstract: To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An F-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

Keywords: emotion dictionary; psychological emotion analysis; conditional random field.

DOI: 10.1504/IJDMB.2024.139466

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.3/4, pp.381 - 405

Received: 14 Jul 2023
Accepted: 26 Oct 2023
Published online: 02 Jul 2024 *

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