Predicting life satisfaction based on the emotion words in self-statement texts - Song, Mengyao; Zhao, Nan (Frontiers in Psychiatry, 2023)
Measuring people's life satisfaction in real-time on a large scale is quite valuable in monitoring and promoting public mental health, however, the traditional questionnaire method cannot fully meet this need. This study utilized the emotion words in self-statement texts to train machine-learning predictive models to identify individual’s life satisfaction. The SVR model was found to have the best performance, with the correlation between predicted scores and self-reported questionnaire scores achieving 0.42, and the split-half reliability achieving 0.939. This result demonstrates the possibility to identify life satisfaction through emotion expressions and provide a method to measure public’s life satisfaction online. The word categories selected by the modeling process were Happy (PA), Sorrow (NB), Boredom (NE), Reproach (NN), Glad (MH), Aversion (ME) and N (negation + positive), which reveals the specific emotions in self-expression relevant to life satisfaction.