Emotion Analysis and Happiness Evaluation for Graduates During Employment - Hang, Lanlv; Zhang, Tianfeng; Wang, Na (Frontiers in Psychology, 2022)
Happiness can be regarded as an evaluation of life satisfaction. A high level of well-being can promote self-fulfillment and build a rational, peaceful, self-esteem, self-confidence, and positive social mentality. Therefore, the analysis of happiness factors is of great significance for the continuous improvement of the individual's sense of security and gain, and the realization of the maximization of self-worth. Emotional performance is not only an important individual's internal factor that affects happiness, but it can also accurately reflect the individual's happiness. However, most of current happiness evaluation methods based on the emotional analysis belong to shallow learning paradigm, making the deep learning method unexploited for automatically happiness decoding. In this article, we analyze the emotions of graduates during their employment and study its influence on personal happiness at work. We propose deep Restricted Boltzmann Machine (DRBM) for graduates’ happiness evaluation during employment. Furthermore, to mitigate the information loss when passing through many network layers, we introduce the skip connections to DRBM and propose a deep residual RBM (DRRBM) for enhancing the valuable information. To verify the effectiveness of the proposed method on the happiness evaluation tasks, we conduct extensive experiments on the statistical data of the China Comprehensive Social Survey (CGSS). Compared to the state-of-the-arts, our method shows better performance, which proves the practicability and feasibility our method in the task of happiness evaluation, and is of great significance for analyzing the emotions of graduates during their employment.